Object-oriented knowledge base system

ABSTRACT

A useful object-oriented knowledge base system is provided, which comprises an ‘object-oriented knowledge base’, an inference mechanism, and an ideal dictionary, etc. Sentences used as a ‘rule’ and/or as a ‘fact’ in the ‘object-oriented knowledge base’ are described according to a simple English grammar. Hierarchical structure of nouns-system in an ‘ideal thesaurus’ is constructed, on the basis of special kind of ‘object-oriented-lexical-definition of nouns’ recorded in the ideal dictionary. Lexical meaning of a verb whose meaning is specific are derived from that of a verb whose meaning is general and universal, by using ‘dichotomy’ on the basis of C-language-like way of description of English sentences in the lexicon. The hierarchical structure of verbs-system in an ‘ideal classification table’ is constructed on the basis of them. The Inference mechanism processes not only mathematically well defined equations but, also simple English sentences, by making full use of the ‘ideal thesaurus’ and the ‘ideal classification table’, on the basis of specially contrived ‘sentence based object-oriented categorical syllogism’.

TECHNICAL FIELD

[0001] This invention relates to object-oriented knowledge base systems.

BACKGROUND ART

[0002] Smart explanation and commentary on the technical term ‘knowledgebase’ is given in {circle over (∘)}“Joho Sisutemu Handobukku” (InJapanese). It is summarized as follows:

[0003] Roughly speaking, a knowledge base is an integrated items ofknowledge (expert knowledge and/or empirical knowledge, etc.) of realworld (usually of a restricted domain of the real world) which areusually stored in computer systems and are described in a form readilyused for the purpose of, say, inference.

[0004] Items of knowledge in a knowledge base can be roughly classifiedinto ‘facts’ and ‘rules’. For example, the knowledge that “A crow is abird” is an example of a ‘fact’, and the knowledge that “Any bird fliesin the air” (i.e. the knowledge that “No matter what a creature may be,the creature flies in the air if the creature is a bird.”) is an exampleof a ‘rule’. If an ‘inference’ is carried out on the bases of these twoitems of knowledge, the answer to the question, for example “Dose a crowfly?” can be answered, if a perfect knowledge base system whose abilitymatches human ability of thought would exist and are used. A portioncarrying out such inferences like this is usually called ‘inferencemechanism’.

[0005] Usually, the main purpose to construct a knowledge base is togive solutions to varieties of problems by using the knowledge base incombination with an inference mechanism. If a very universal inferencemechanism exists, then, procedures for solving problems have notnecessarily be explicitly described as computer programs; That is, if aknowledge base system has a very universal inference mechanism, then auser of the knowledge base system can solve problems only byrepresenting the necessary items of knowledge according to the style ofrepresentation of the knowledge base. Therefore, if so, the user may notnecessarily be skillful at coding computer programs.

[0006] An important issue to be discussed when a knowledge base isconstructed, is the way of ‘knowledge representation’. As ideal meritsthat a good ‘knowledge representations’ should have, for examples,

[0007] 1) Having high power of expression and being able to describevarieties of things and matters in a systematic manner.

[0008] 2) Having high readability and being easily understood by human.

[0009] 3) Having high degree of modularity and being highly flexible tothe renewal of items of knowledge stored in the knowledge base.

[0010] 4) With which, being able to implement (=embody) high performanceprocessing systems.

[0011] Four types of methods for knowledge representation are broadlyknown:

[0012] 1) Predicate logic.

[0013] 2) Production rule.

[0014] 3) Semantic network.

[0015] 4) Frame.

[0016] The ‘semantic network’ model is originally introduced to describehuman memory and ability of association in the field of cognitivepsychology (see for example {circle over (∘)}“Gurafikku Ninchishinrigaku” p.86), but is frequently used also in the field ofartificial intelligence: (see for example, {circle over (∘)}“The CLASSICKnowledge Representation System or, KL-ONE”).

[0017] The KL-ONE is one of the most famous knowledge representationsystems. The KL-ONE's root is in ‘semantic network’, but KL-ONE isinfluenced in part by ‘Frame’. The CLASSIC Knowledge Representationsystem is a new generation of KL-ONE-like systems ({circle over (∘)}“TheCLASSIC Knowledge Representation System or, KL-ONE”). The KL-ONE and itsdescendants are one of the most long-lived knowledge representationsystems, and its research has lasted over two decades at least up totoday. For more details, see for example, recent US Patents

[0018] {circle over (∘)}“U.S. Pat. No. 5,974,405 Oct. 26, 1999 Knowledgebase management system with enhanced explanation of derived informationand error objects”, and,

[0019] {circle over (∘)}“U.S. Pat. No. 5,659,724 Aug. 19, 1997Interactive data analysis apparatus employing a knowledge base”.

[0020] As the fifth type of method for knowledge representation,

[0021] 5)‘Object-oriented knowledge base’

[0022] is known.

[0023] The original idea ‘Object’ was first introduced when a computerlanguage ‘Simula67’ was designed. The Simula67 got extremely highreputation as a computer simulation system (Chapter 2 of {circle over(∘)}“MODELLING the WORLD with OBJECTS”). Thus, the original idea,‘Object’ was born primary as a concept of programming language (i.e.Simula67) rather than as a method only for knowledge representation({circle over (∘)}“Chishiki no hyo-gen to kousoku suiron”, p.11).

[0024] The term ‘object-oriented programming language’ is a general andsomewhat vaguely used term, and in many cases represents a programminglanguage supporting the way to implement (=embody) an ‘object’, which isusually represented in a source code of a computer program as anintegrated unit composed of algorithms and data ({circle over (∘)}“JohoSisutemu Handobukku” p.4-202). The SIMULA67 is of course anobject-oriented programming language. Smalltalk and C++ are alsoobject-oriented programming languages. Usually, each of “graphicalobjects” in a GUI (Graphical User Interfaces) of computers, such asicons, windows, dialog boxes, Mouse cursors etc. are usually implementedby using object oriented languages. In such cases, above mentioned typeof an integrated unit composed of algorithms and data defined in thesource code of the object oriented software supporting the GUI, isusually used to embody a “graphical object”.

[0025] Some persons believe in that object-oriented representation isjust suitable to be used not only as a method to implement “graphicalobjects” in a GUI, but also as a method of knowledge representations ina knowledge base system ({circle over (∘)}“MODELLING the WORLD withOBJECTS” Chapter 12). It is highly desirable today to propose varietiesof types of object-oriented knowledge base systems of practical useuntil the ultimate and perfect solution is got.

[0026] As a matter of fact, some pioneering and challenging work toconstruct object-oriented knowledge base systems have been done,including, O-logic, ({circle over (∘)}“A Logic for Object-Oriented LogicProgramming (Maier's O-Logic: Revisited)”, and, Transaction Logic({circle over (∘)}“Transaction Logic Programming (or, a Logic ofProcedural and Declarative Knowledge”), and, Quixote({circle over(∘)}“Specific Features of a Deductive Object-Oriented Database LanguageQuixote”). Knowledge representations used in these knowledge basesystems are very precise and are extremely mathematical.

[0027] However, these object-oriented knowledge base system is not sopopular when compared with other software systems, say, ‘©MS-DOS’,‘©Windows 95’, ‘©Office’ (presented by Microsoft). It is highlydesirable to give object-oriented knowledge base systems with facts andrules having high readability and being easily understood by human topresent a knowledge base system of very wide and popular use. One of thefinal purposes to be attained in developing an ultimate object-orientedknowledge base system is to give

[0028] a knowledge base system in which

[0029] a flexible, systematic, exact, exhaustive, and user-friendly wayof ‘knowledge representation’ which covers not only what can bedescribed mathematically but also covers what can be describedlinguistically, and,

[0030] a mighty ‘inference mechanism’ for inference widely applicable towide varieties of sentences in the ‘knowledge representation’, coexist.

DISCLOSURE OF INVENTION 1. SUMMARY

[0031] A useful object-oriented knowledge base system is provided, whichcomprises an ‘object-oriented knowledge base’, an inference mechanism,and an ‘object-oriented knowledge base management system’. In theknowledge base, simple English sentences are used as a rule and/or as afact. An ‘ideal dictionary’ exists in the ‘object-oriented knowledgebase management system’, wherein an object-oriented-lexical-definitionof nouns is given, and dichotomy in combination with c-language-like wayof description of English sentences is used to give a lexical definitionof a verb. The ‘ideal dictionary’ is used as a basis on which an ‘idealthesaurus’ and an ideal ‘classification table’, in the ‘object-orientedknowledge base’, are constructed. The inference mechanism, which isbased on a specially contrived ‘object-oriented categorical syllogism’,processes not only mathematical equations but also simple Englishsentences, by making full use of a thesaurus and a classification table.

2. OBJECTS AND ADVANTAGES

[0032] Accordingly, several objects and advantages of my invention are:

[0033] (a) My invention provides a way to describe facts and rules usinga sentence having high readability and flexibility and being easilyunderstood by human, because such a sentence is usually described in alinguistic style on the basis of simple grammar.

[0034] (b) My invention provides a way to construct a hierarchicalstructure of nouns-system in a ‘thesaurus’, on the basis of special kindof ‘object-oriented-lexical-definition of nouns’ recorded in an ‘idealdictionary’ of the knowledge base. Lexical definition of a verb is alsogiven in an ‘ideal dictionary’ of the knowledge base. Lexical definitionof a verb whose meaning is specific is derived from that of a verb whosemeaning is general and universal, by specialization of the meaning byusing ‘dichotomy’ and/or by using C-language-like way of linguisticdescription of sentences in the lexicon. A hierarchical structure ofverbs-system is constructed in a ‘classification table’ on the basis ofthis lexical definition of verbs.

[0035] (c) My invention provides exact and systematic ‘knowledgerepresentation’ if

[0036] only the nouns and the verbs that are explicitly defined in an‘ideal dictionary’ and are registered in a thesaurus and/or in aclassification table are used

[0037] in the sentences used for ‘knowledge representation’.

[0038] (e) My invention provides exhaustive power of description of thetarget to be modeled, on the basis of the present ‘knowledgerepresentation’, because the power of description not only covers amathematically well-defined equation, a law of Physics, and a subroutineof a computer program, but also covers a sentences that are describedlinguistically as a special kind of ‘function’. In the description ofthe target to be modeled is essentially linguistic, in which manyvarieties of verbs and nouns as well as mathematical symbols including‘=’, ‘×’, ‘÷’, etc. are flexibly used in a systematic manner.

[0039] (f) My invention provides an versatile inference mechanism, whichcan deal with not only mathematically well defined equations, laws ofPhysics, mathematical lemma, and subroutines of computer programs, butalso varieties of linguistic sentences using many varieties of verbs bymaking full use of a ‘thesaurus’ and a ‘classification table’, on thebasis of specially contrived ‘object-oriented categorical syllogism’. Myinvention provides precise inference mechanism on the basis oflinguistics, if ‘subject-word (S)’, ‘verb (V)’, ‘complement-word (C)’,‘object-word (O)’, ‘indirect-object-word (I.O)’, and/or‘direct-object-word (D.O)’, are precisely and explicitly indicated in asentence used as a ‘rule’ and/or as a ‘fact’ in the presentobject-oriented knowledge base system.

[0040] Other objects and advantages are that my invention provides

[0041] a means for storing data providing the ability of association,

[0042] which are used as the main basis on which

[0043] “means for making a list of ‘descriptors’ ranked in order of hitfrequency” and/or

[0044] “means for making a list of ‘names-of-classification-items’ranked in order of hit frequency”

[0045] are carried out. These means help a user of the object-orienteddatabase system to find descriptors and/or to findnames-of-classification-items to be used to make a query.

[0046] Other objects and advantages are that my invention provides ameans to prevent a combinatorial explosion during carrying out saidmeans for carrying out a inference, by using not only

[0047] ‘means for narrowing down the target ‘descriptors’’ and ‘meansfor narrowing down the target ‘names-of-classification-items’’, formaking a precise retrieval,

[0048] but also

[0049] ‘means for fusing propositions’, in which many numbers ofpropositions are summarized into a sentence by making use ofhierarchical structures of nouns and verbs, making some sacrifice ofstrictness for preventing the puncture of the processing capacity of thecomputer system.

[0050] Other objects and advantages are that my invention provides ameans for making more exhaustive retrieval, by using ‘means forbroadening out the target ‘descriptors’’ and/or by using ‘means formeans for broadening out the target ‘names-of-classification-items’’.

[0051] Further objects and advantages of my invention will becomeapparent from a consideration of the drawings and ensuing description.

3. DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT

[0052] An object-oriented knowledge base systems presented in thepresent invention has an ‘object-oriented knowledge base’, whichcomprises ‘rules’, data mainly used to providing the ability ofassociation, an ‘ideal thesaurus’, and an ‘ideal classification table’(See FIG. 1). And an object-oriented knowledge base systems presented inthe present invention has a tool to construct a hierarchical system ofverbs in an ‘ideal classification table’, on the basis of an ‘idealdictionary’, and tool to construct a hierarchical system of nouns in an‘ideal thesaurus’, on the basis of an ‘ideal dictionary’. And anobject-oriented knowledge base systems presented in the presentinvention has an inference mechanism. The body of information embodyingan object-oriented knowledge base systems presented in the presentinvention, such as computer programs and/or contents of knowledge, arestored in a ‘means for storing knowledge base system’ (See FIG. 1).

[0053]

Lexical Definition of ‘means for storing knowledge base system’

A solid thing to be used to store the body of information of anobject-oriented knowledge base system is a ‘means for storing knowledgebase system’. For example, a storing media, a memory, and/or an ASIC(Application Specific Integrated Circuit) is a ‘means for storingknowledge base system’. The body of information of an object-orientedknowledge base system may be stored in one ‘means for storing knowledgebase system’. And/or the body of information of an object-orientedknowledge base system may be separated into many different parts, andthese parts may be distributed and recorded in plurality of ‘means forstoring knowledge base system’.

[0054] This lexical definition is schematically shown in FIG. 21.

[0055]

Lexical Definition of a storage media

An article and/or a substance and/or material in which and/or on whichinformation can be stored as a form of them and/or as a state of them,is a storage media. For example, a Hard disk, a Floppy disk, a CD(Compact Disk), a MO (Magnetic Optical Disk), a CDR (CD Recordable), aCDRW (CD Rewritable), and/or a DVD (Digital Versatile Disk), and amemory card is a storage media.

[0056] This lexical definition is schematically shown in FIG. 21.

[0057]

Lexical Definition of memory

If and when a device such as a computer has a part used to storeinformation, then, the part is a memory. For example, a ROM (Read OnlyMemory), and/or a RAM (Random Access memory), is a memory.

[0058]

Lexical Definition of record

A user and/or a maker of a database treats and/or processes informationuses a record, as a unit of data in a database and/or in a knowledgebase.

[0059]

Lexical Definition of key

One and/or more than two items of words, clauses, and/or sentences etc.which can be suitably used to distinguish and/or to discriminate and/orto characterize a record of a data in a database, is a key.

[0060] Forms of knowledge representations (i.e. data structures) used inthis knowledge base include,

[0061] “sentence pattern of association”,

[0062] “sentence pattern of definition of object”,

[0063] “sentence pattern of ‘ideal thesaurus’”,

[0064] “sentence pattern of a list of the names of the lexical meaningsof a natural word”,

[0065] “sentence pattern of implementation of names ofalgorithms-of-processes”,

[0066] “sentence pattern of classification”,

[0067] “sentence pattern of physical and/or mathematical rules”,

[0068] “sentence pattern of function”, and

[0069] “sentence pattern of instances of solving problems”.

[0070] Detailed explanations and lexical definitions of these forms ofknowledge representations will be given step by step, afterwards.

[0071] Inference mechanisms used in the object-oriented knowledge basesystems disclosed in the present invention comprises,

[0072] @[algorithm of sentence based object-oriented categoricalsyllogism],

[0073] @[algorithm of sentence based object-oriented hypotheticalsyllogism],

[0074] @[algorithm of making a list of ‘descriptors’ ranked in order ofhit frequency],

[0075] @[algorithm of making a list of ‘names-of-classification-items’ranked in order of hit frequency],

[0076] @[algorithm of narrowing down the target ‘descriptors’ and/ortarget ‘names-of-classification-items’] and/or @[algorithm of fusingpropositions], and,

[0077] @[algorithm of broadening out the target ‘descriptors’ and/ortarget ‘names-of-classification-items’].

[0078] Detailed explanations and lexical definitions of these algorithmswill be given step by step afterwards.

[0079] Here I only mention that these knowledge representations andthese inference mechanisms forms a knowledge base system based on aclose relation among thesauruses, categories, concepts and dichotomy.

3.1. Measures to Deal with the Problem of Polysemous Words 3.1.1.Problem of Polysemous Words in Object-Oriented Knowledge Base Systems

[0080] Generally speaking, object-oriented knowledge base systems sharemany technical issues to be solved with database systems. For example,when a useful database system and/or a useful knowledge base system isto be designed, the ‘problem of polysemous words’ must be took intoconsideration; A man has an ability to express one idea in variouswords. This ability brings about a rich humane ability of speech, but onthe other hand, causes omission in the uniformity of the way ofexpression. Unevenness in usage in words by users of a database systemmay cause the users to fail in retrieving all the necessary informationout of a database system. In other words, if a maker of contents of adatabase system and a user of the database system express one idea indifferent words, then, the idea will not be retrieved during theretrieval carried out by the user.

3.1.2. Short Comment on Thesauruses Used in the Present Object-OrientedKnowledge Base System

[0081] In many database systems, the principal reason why a thesaurus isconstructed is to prevent such imperfect retrieval, by storingwell-defined key words on the thesaurus in a systematic way. As theresult, the description with which one idea is expressed is unified.Roughly speaking, thesaurus used in the present object-orientedknowledge base system is a text in which key words are put into groupswith other key words that have similar meanings. In many cases, a keyword is defined to represent this ‘similar meanings’. This hierarchicalstructure of key words in a thesaurus helps the user of the presentobject-oriented knowledge base system to find just the appropriate keyword in a systematic way.

[0082] Let us assume an example of a thesaurus that contains groupsdenoted by the key words, ‘physics’, ‘biology’, ‘chemistry’, etc. Then,it is reasonable to use a key word ‘science’ as the name of the group ofthe key words. I describe this situation that “‘physics’, ‘biology’,‘chemistry’, etc. are a narrower key word of ‘science’”. In other words,‘science’ is the broader key word of ‘physics’, ‘biology’, ‘chemistry’,etc. I describe this situation as,

[0083] “‘physics’ is a kind of ‘science’”, and/or

[0084] “‘biology’ is a kind of ‘science’”, and/or

[0085] “‘chemistry’ is a kind of ‘science’”, etc.

[0086] Of course, it is also reasonable to registered in the thesaurusseveral additional key words as narrower key words of each of ‘physics’,‘biology’, ‘chemistry’, etc. For example, key words such as ‘mechanics’and/or ‘dynamics’ may be regarded as a narrower key word of ‘physics’.In other words, key words such as ‘mechanics’ and/or ‘dynamics’ may beregarded as a member of the group whose name is ‘physics’. Furthermore,in the similar way, key words such as ‘Newtonian mechanics’ and/or‘quantum mechanics’ may be registered in the thesaurus as a narrowerdescriptor of ‘mechanics’. And ‘Botany’ and/or ‘zoology’ may beregistered in the thesaurus as a narrower descriptor of ‘biology’.

[0087] This situation is described in a schematic way as, ‘science’‘physics’, ‘mechanics’ ‘Newtonian mechanics’   ‘quantum mechanics’ ‘dynamics’  ‘biology’,  ‘botany’  ‘zoology’ ‘chemistry’

[0088] As is shown in this example, in a thesaurus used in the presentobject-oriented knowledge base system, a hierarchical structure is givenexplicitly to the set of key words, on the basis of relations between abroader key word and a narrower key word. In the present invention,lexical definition of these terms ‘broader’ and ‘narrower’ will be givenlater in a formal way on the basis of what I call “sentence pattern ofdefinition of object” (See the “§3.2.1.5. Mathematical foundations forDefinition of ‘Thesaurus’” of the present invention.). And in thepresent invention, I will give a precise lexical definition of ‘ideathesaurus’ later in the present invention. I just claim here that agroup of nouns in a thesaurus used in an object-oriented knowledge basesystem disclosed in the present invention is just an example of what Icall a ‘category’ of nouns.

3.1.2.1. ‘Ideal Nouns’

[0089]

Lexical Definition of ‘ideal noun’

In most cases, a noun used in a natural language (i.e. a ‘natural-noun’)is a polysemous noun, and has several lexical meanings. I regard a nounphrase a kind of noun in the present invention. If one uses a noun in anobject-oriented knowledge base system disclosed in the presentinvention, then, it is recommended that he should give a lexicaldefinition of the noun in a dictionary of the system. When a naturalpolysemous noun is defined in a dictionary of the presentobject-oriented knowledge base system, it is recommended to give a nameto each of its lexical meanings. I call such a name an ‘ideal noun’, inthe sense that the name is ideal from the standpoint of logic. By thislexical definition, of course, an ‘ideal noun’ has only one lexicalmeaning.

[0090] For example, I will show below a case in which I regard a naturalnoun ‘abuse’ as an example of a polysemous noun.

[0091] One of the lexical meanings of the polysemous noun ‘abuse’ is“the use of something in a way that it should not be used” (See LONGMNdictionary of contemporary English). I name this lexical meaning ‘abuse

usage

’. That is, ‘abuse

usage

’=“the use of something in a way that it should not be used”. By mydefinition given just above, ‘abuse

usage

’ is what I call an ‘ideal noun’.

[0092] Another lexical meaning of the polysemous noun ‘abuse’ is “rudeor offensive things that someone says to someone else”. And I name thislexical definition ‘abuse

word

’. That is,

[0093] ‘abuse

word

’=“rude or offensive things that someone says to someone else”.

[0094] By my definition given just above, ‘abuse

word

’ is another example of what I call an ‘ideal noun’.

[0095] Still another lexical meaning of the polysemous noun ‘abuse’ is“cruel or violent treatment, often sexually, especially someone that youshould look after”. And I name this lexical definition ‘abuse

treatment

’. That is,

[0096] ‘abuse

treatment

’=“cruel or violent treatment, often sexually, especially someone thatyou should look after”.

[0097] By my definition given just above, ‘abuse

treatment

’ is still another example of what I call an ‘ideal noun’.

[0098] Here, the word ‘ideal’ means ‘strict from the view point oflogic’.

[0099] I claim that in ideal cases, one lexical meaning of a polysemousnoun equals to ‘one set of the particular qualities characterizing acategory which is used to represent linguistically an idea; Here, theword idea means ‘the idea of image of an ‘object’’. The detailedexplanation of what I call an ‘object’ will be given in the “§3.2.1.1.Details of My Interpretation of the Meaning of ‘Object’” of the presentinvention. In a word, in ideal cases, an ‘ideal noun’ is the strict nameof an object.

3.1.2.2. ‘Ideal nouns’ used as ‘Descriptors’

[0100]

Lexical Definition of ‘descriptor’

A key word listed in an ‘thesaurus’ of an object-oriented knowledge basesystem disclosed in the present invention is a ‘descriptor’. In mostcases, a thesaurus of an object-oriented knowledge base system disclosedin the present invention is used to classify ‘ideal nouns’ used as keywords for the object-oriented knowledge base system disclosed in thepresent invention. It is recommended that as a ‘descriptor’, an ‘idealnoun’ should be used. But an ‘ideal noun’ is not necessarily a‘descriptor’. That is an ‘ideal noun’ is judged not to be a ‘descriptor’if the ‘ideal noun’ is not listed in a thesaurus of an object-orientedknowledge base system disclosed in the present invention.

[0101] For example, if a sentence like,

[0102] ‘abuse

usage

’ is a kind of ‘usage’,

[0103] is used in an ‘thesaurus’ of an object-oriented knowledge basesystem disclosed in the present invention, to show that ‘abuse

usage

’ is a narrower key word of ‘usage’, then both an ‘ideal noun’, ‘abuse

usage

’ and an ‘ideal noun’, ‘usage’ are regarded as a ‘descriptor’ in theobject-oriented knowledge base system.

[0104] Else if an ‘ideal noun’ ‘abuse

usage

’ and/or an ‘ideal noun’, ‘usage’ is not used in a thesaurus of anobject-oriented knowledge base system disclosed in the presentinvention, then the ‘ideal noun’, ‘abuse

usage

’ and/or the ‘ideal noun’, ‘usage’ is judged not to be a ‘descriptor’ inthe object-oriented knowledge base system.

3.1.2.3. “Lexical Definitions of ‘Ideal Nouns’” Given in ‘IdealDictionaries’

[0105] Before giving the lexical definition of an ‘ideal dictionary’,let me show here an example of a way in which how a natural polysemousnoun, ‘abuse’, is defined in an ‘ideal dictionary’. As I mentionedbefore, a natural polysemous noun, ‘abuse’ has three lexical meanings. Igive a name to each of these three lexical meanings of the naturalpolysemous noun‘abuse’. These names are ‘abuse

word

’, ‘abuse

usage

’, and, ‘abuse

treatment

’.

[0106] In other words, as I mentioned before, in the present example,these three names are used as an ‘ideal noun’ denoting a lexical meaningof the natural polysemous noun ‘abuse’. In an ‘ideal dictionary’, first,it is recommended that these three ‘ideal nouns’ should be listed in asentence described in what I call a “sentence pattern of a list of thenames of the lexical meanings of a natural word”. The lexical definitionof “sentence pattern of a list of the names of the lexical meanings of anatural word” will be given later in the present invention. But here, Ijust only show, as an example, a sentence described in “sentence patternof a list of the names of the lexical meanings of a natural word” forthe natural polysemous noun ‘abuse’ in a simplified form:

[0107] _ListOfLexicalMeaningsOf_abuse_—>_(— —)

Noun_KW

_=_(— —)(abuse

usage

)_,_(abuse

word

)_,_(abuse

treatment

)_.

[0108] The complete form of a sentence described in “sentence pattern ofa list of the names of the lexical meanings of a natural word” for thenatural polysemous word ‘abuse’ will be shown later in the presentinvention. According to a lexical definition given later in the presentinvention, I call a sentence written in “sentence pattern of a list ofthe names of the lexical meanings of a natural word”, a ‘means forstoring the list of lexical meanings of a natural word’. For detail, seethe lexical definition of “sentence that store the list of lexicalmeanings of a natural word” and the lexical definition of ‘means forstoring the list of lexical meanings of a natural word’, which will begiven later in the present invention. In a word, “a key described using‘means for storing the list of lexical meanings of a natural word.’” isone of the components of an ‘ideal dictionary’ whose constitution is arecommended constitution (See FIG. 6).

[0109] It is recommended that an ‘ideal dictionary’ should have anothercomponent. That is, it is recommended that an ‘ideal dictionary’ shouldcomprise not only “a key described using ‘means for storing the list oflexical meanings of a natural word’” but also what I call “keys givinglexical definition of a lexical meaning”. (See FIG. 6)

Lexical Definition of a “Key Giving Lexical Definition of a LexicalMeaning”

[0110] A “key giving lexical definition of an ‘ideal noun’” and/or a“key giving lexical definition of an ‘ideal verb’” is a “key givinglexical definition of a lexical meaning”. This lexical definition isschematically shown in FIG. 6.

[0111] The lexical definition of a “key giving lexical definition of an‘ideal noun’” and the lexical definition of a “key giving lexicaldefinition of an ‘ideal verb’” will be given later in the presentinvention. Here, now, first, I will give an example in which the way thelexical definition of the ‘ideal noun’, ‘abuse’ is given.

[0112] As the lexical meaning of an ‘ideal noun’, for example, ‘abuse

word

’, in some dictionaries such as {circle over (∘)}“Longman Dictionary ofContemporary English”, sentence like

[0113] “Abuse

word

is an offensive word, which often upsets people”,

[0114] is used. In the present invention, I analyze this lexical meaninginto three sentences,

[0115] 1) ‘Abuse

word

’ is a kind of word.

[0116] 2) Ethical nature of ‘abuse

word

’ is judged to be and/or is felt to be vicious.

[0117] 3) ‘Abuse

word

’ makes people upset.

[0118] What I call here ‘ethics’ exclusively means a rule to get out ofthe dilemma between public benefit and individual benefit. Definition ofthe special meaning of ‘ethics’ used here is given mathematically on thebasis of the game theory. See for example, {circle over (∘)}“PRISONER'SDILEMMA”(§ of “prisoner's dilemma which appear in literatures”).

[0119] By my definition that will be given later in the presentinvention, “sentence 1)” is an example of a “sentence that stores dataof ideal thesaurus”. It is recommended that in an ‘ideal dictionary’,the “sentence 1)” should be formalized by paraphrasing the “sentence 1)”into a sentence described in what I call “sentence pattern of ‘idealthesaurus’”. The lexical definition of “sentence pattern of ‘idealthesaurus’” will be given later in the present invention. Here, I justonly show, as an example, a sentence described in “sentence pattern of‘ideal thesaurus’” for “sentence 1)” in a simplified way:

[0120] _NT_(——)(abuse

word

)_(——)is_a_kind_of_BT_(——)(word)_.

[0121] The complete form of a sentence described in “sentence pattern of‘ideal thesaurus’” for “sentence 1)” will be shown later in the presentinvention. According to my lexical definition given later in the presentinvention, a “sentence that stores data of ideal thesaurus” is called a‘means for storing data of ideal thesaurus’. In an object-orientedknowledge base system, however, ‘means for storing data of idealthesaurus’ is not necessarily an indispensable component of an ‘idealdictionary’, because, an ‘ideal thesaurus’ almost always contains ‘meansfor storing data of ideal thesaurus’. Therefore, in the ‘idealthesaurus’ schematically shown in FIG. 6, the component, “keys describedusing ‘means for storing data of ideal thesaurus’, is omitted.

[0122] I regard the “sentence 2)” and the “sentence 3)” as sentenceswhich describes the definition of the ‘ideal noun’, ‘abuse

word

’, in an object-oriented style. Details of my interpretation of themeaning of ‘object’ will be shown later in Section “§3.2.1.1. Details ofMy Interpretation of the Meaning of ‘Object’”. By my definition givenlater in the present invention, “sentence 2)” and “sentence 3)” is whatI call a “sentence that stores data that define object”. According to mydefinition given later in the present invention, a “sentence that storesdata that define object” is a ‘means for storing data that defineobjects’. In general, it is recommended that “keys described using‘means for storing data that define objects’” should be used as a “keygiving lexical definition of an ‘ideal noun’” in an object-orientedknowledgebase system disclosed in the present invention. This isschematically shown in FIG. 6. More specifically, it is recommended inan object-oriented knowledge base system, that “keys described using‘means for storing data that strictly define objects’” should be used asa “key giving lexical definition of an ‘ideal noun’”. The lexicaldefinition of ‘means for storing data that strictly define objects’ willbe given later in the present invention. This is also schematicallyshown in FIG. 6. But I will show here now, how these “sentence 2)” and“sentence 3)” are described using a sentence in what I call a “sentencepattern of definition of object”:

[0123] _OBJECT_(——)(abuse

word

)_have_VARIABLES ethical nature which_is judged and/or is felt to bevicious, and

[0124] _OBJECT_(——)(abuse

word

)_(——)have_FUNCTION_to upset people.

[0125] About this issue, a detailed discussion will be given later inthe present invention. A similar discussion made thus far for ‘abuse

word

’, can also be given to ‘abuse

usage

’, and, ‘abuse

treatment

’.

[0126] Roughly speaking, I claim that a polysemous ‘natural-noun’corresponds to what I call a ‘concept’, and each lexical meaning of thepolysemous ‘natural-noun’ corresponds to a ‘category’. Therefore, here,an ‘ideal noun’ is the name of a ‘category’.

[0127] I claim that in ideal cases, ‘an object’ corresponds to ‘acategory’. Speaking more precisely, in other words, in ideal cases, Iregard an ‘ideal noun’ as the name of an ‘object’. From my definitiongiven above, in ideal cases, I regard a ‘descriptor’ is also the name ofan ‘object’.

Lexical Definition of a “Key Giving Lexical Definition of an ‘IdealNoun’”

[0128] A “key giving lexical definition of an ‘ideal noun’” is asentence which gives lexical definition of an ‘ideal noun’. It isrecommended that a “key described using ‘means for storing data thatstrictly define objects’” should be used as a “key giving lexicaldefinition of an ‘ideal noun’” in an object-oriented knowledge basesystem disclosed in the present invention.

[0129] This lexical definition is schematically shown in FIG. 6.

[0130] The lexical definition of ‘means for storing data that strictlydefine objects’ will be given later in the present invention.

[0131]

Lexical Definition of an ‘ideal dictionary’ (Part 1: about lexicaldefinition of ‘ideal nouns’)

An ‘ideal dictionary’ is a kind of dictionary. When the lexical meaningof an ‘ideal noun’ is given in an ‘ideal dictionary’, then, it isrecommended that a ‘means for storing data that define objects’ shouldbe used.

[0132] An ‘ideal dictionary’ is usually used in an object-orientedknowledge base system disclosed in the present invention.

[0133] It is recommended that in an ‘ideal dictionary’, ‘the content’ of‘each of the lexical meanings of a ‘natural-noun’’ (i.e. ‘the content’of ‘one set of the particular qualities characterizing a category’ whichis used to represent linguistically ‘an idea of image of some object’)should be explained in sentences in what I call a “sentence pattern ofdefinition of object”. And/or in some cases, as mentioned just before,sentences in what I call “sentence pattern of ‘ideal thesaurus’” may beused as ‘the content’ of ‘each of the lexical meanings of a‘natural-noun’’ when the object-oriented knowledge base system disclosedin the present invention is small. About the lexical definition of“sentence pattern of definition of object” will be given later in thepresent invention. The lexical definition of “sentence pattern of ‘idealthesaurus’” will be given later in the present invention.

[0134] Remember that it is recommended that the information about thelist of the ‘ideal nouns’ corresponding to the lexical meanings of anatural polysemous noun, should be described in a sentence. And it isfurther recommended that the sentence should be described in what I call“sentence pattern of a list of the names of the lexical meanings of anatural word”, which is a special pattern of a sentence writtenaccording to the English grammar. Such a sentence may be included in an‘ideal dictionary’ when the object-oriented knowledge base system issmall. When the object-oriented knowledge base system is large, only thepointer to the sentence should be recorded in an ‘ideal dictionary’, Seethe discussion given later in the present invention about the lexicaldefinition of “sentence pattern of a list of the names of the lexicalmeanings of a natural word”. Now let me describe the same thing in amore formal way; According to my lexical definition given later in thepresent invention, a sentence described in “sentence pattern of ‘idealthesaurus’” is called a ‘means for storing data of ideal thesaurus in aformal way’. But as mentioned just above, ‘means for storing data ofideal thesaurus in a formal way’ is not an essential part of an ‘idealdictionary’, because the same information is usually stored in an ‘idealthesaurus’ (See FIG. 2). It should be noted that the description in an‘ideal dictionary’ and an ‘ideal thesaurus’ should be consistent. One ofthe best way of keeping such consistency when the system is very large,is to eliminate all the sentences described in “sentence pattern of‘ideal thesaurus’” from an ‘ideal dictionary’, when the object-orientedknowledge base system disclosed in the present invention is very large.Only the pointers to the sentences described in “sentence pattern of‘ideal thesaurus’” from an ‘ideal dictionary’ should be recorded in an‘ideal dictionary’ when the system is large.

[0135] Of course, other media than characters and/or letters in a text,for example, such as pictures, photos, sounds, and/or other multimediamay be used as an accessory of an ‘ideal dictionary’.

[0136] The way in which the lexical definition of an ‘ideal verbs’ in an‘ideal dictionary’ will be described later in the present invention.

3.1.2.4. ‘Ideal Thesaurus’

[0137] In most of contemporary thesauruses used for on-line databasesystems, basically, only descriptors are registered systematically, anda hierarchical structure of descriptors are stored in such a thesaurus.However, in some of the book of thesauruses which are available at abook shop, (e.g. {circle over (∘)}“Roget's International thesaurusfourth edition”, {circle over (∘)}“Roget's International thesaurus fifthedition”), on the other hand, not only descriptors, but also naturalwords are listed (Strictly speaking, no distinction between naturalwords and descriptors are not made in the two books). From my analyticalpoint of view disclosed in the present invention, I claim that a user ofsuch a book of thesaurus can track the hierarchical structure definedamong categories (i.e., among what I call ‘descriptors’ in the presentinvention) from top to bottom, and at last, he will find a natural word(i.e., what I call a concept in the present invention). I claim that{circle over (∘)}“Roget's International thesaurus fourth edition” and/or{circle over (∘)}“Roget's International thesaurus fifth edition” can beregarded basically as a book to consult natural words (i.e., concepts)from user's ideas (i.e., categories). As a matter of fact, {circle over(∘)}“Roget's International thesaurus fourth edition” and/or {circle over(∘)}“Roget's International thesaurus fifth edition” stores not onlywords but also categories.

[0138] On the other hand, what I call an ‘ideal thesaurus’ contains only‘descriptors’. And, natural nouns are usually not an indispensablecomponent of an ‘ideal thesaurus’.

[0139] <<Lexical Definition of an ‘ideal thesaurus’>> An ‘idealthesaurus’ is a thesaurus in which ‘ideal nouns’ are registered. It isrecommended that in an ‘ideal thesaurus’, no polysemous nouns should beregistered. In an ‘ideal thesaurus’, it is recommended that a sentencein which an ‘ideal noun’ is registered should be described in what Icall “sentence pattern of ‘ideal thesaurus’”.

[0140] The lexical definition of “sentence pattern of ‘ideal thesaurus’”will be given later in the present invention. I show here just anexample:

[0141] For an example,

[0142] if and when a sentence stored in an ‘ideal thesaurus’ which meansthat

[0143] “abuse

usage

is the narrow term of use

usage

”,

[0144] and/or that

[0145] “abuse

usage

is a kind of use

usage

”,

[0146] is described in a formal way using what I call “sentence patternof ‘ideal thesaurus’”, then, the sentence is described as,

[0147] _NT_(— —)(abuse

usage

)_(— —)is_a_kind_of_BT_(— —)(use

usage

)_.

Lexical Definition of “Sentences that Store Data of Ideal Thesaurus”

[0148] A “sentences that store data of ideal thesaurus” is a sentenceused in an ‘ideal thesaurus’. I call a sentence whose form is,

[0149] “‘***’ is the narrow term of ‘****’”,

[0150] where ‘***’ and ‘****’ are ‘descriptors’,

[0151] and/or its equivalents

[0152] a “sentences that store data of ideal thesaurus”.

[0153] By definition given later in the present invention, a sentencedescribed in “sentence pattern of ‘ideal thesaurus’” is a “sentence thatstores data of ideal thesaurus”.

Lexical Definition of Means for Storing Data of Ideal Thesaurus

[0154] “Sentence which stores data of ideal thesaurus” and/or somethingthat stores the information of it, is a means for storing data of idealthesaurus.

[0155] “Sentences that store data of ideal thesaurus” is used todescribe a ‘fact’ used in an ‘object-oriented knowledge base’ disclosedin the present invention. See FIG. 1 shows schematically the relationbetween a ‘fact’ and an ‘object-oriented knowledge base’.

[0156] See FIG. 2 which shows schematically this issue and other issuesrelated to this issue.

[0157] Therefore, of course, unlike with the {circle over (∘)}“Roget'sinternational thesaurus fourth edition” and/or {circle over (∘)}“Roget'sinternational thesaurus fifth edition”, information about the relationbetween natural words and ‘descriptors’ can not be found in my ‘idealthesaurus’. As a result, of course, basically, not only an ‘idealthesaurus’ but also a set of data which tells the information about therelation between ‘descriptors’ and natural words is necessary, for auser of an object-oriented knowledge base system disclosed in thepresent invention, to transform his concepts (i.e. natural words) into‘descriptors’.

[0158] As most of the natural words are polysemous, context comprisingmore than two natural words instead of a single natural word, arenecessary to specify the meaning of the natural word. As a contextcomprises more than two natural words, more than two descriptorscorrespond to a context, in many cases. Therefore, in most cases, a datawhich contains context comprising more than two natural words and morethan two descriptors corresponding to the context is necessary todescribe the relation between ‘descriptors’ and natural words. As willbe explained later, I regard such data as a kind of data providing theability of association. For example, a lexical meaning of a polysemousnoun ‘bark’ is definite in a context “bark of a dog”, and anotherlexical meaning of the polysemous noun ‘bark’ is definite in a contextof “a bark of a tree”. The lexical definition of “sentences that storedata providing the ability of association” will be given later in thepresent invention. Here I just give an example: The sentence,

[0159] “The word ‘bark’ in the context “bark of a dog” means_(bark

sound

)_, and the word ‘dog’ in the same context means_(dog

animal

)_.”

[0160] is an example of a “sentence that stores data providing theability of association”.

[0161] In the present invention, I recommend that a “sentence whichstores data providing the ability of association” should be recorded asa sentences described in what I call “sentence pattern of association”.The lexical definition of “sentence pattern of association” will begiven later in the present invention. Basically, as will be describedlater, in ideal cases, in a sentence described in “sentence pattern ofassociation”, it is recommended that all the ‘descriptors’ that areassociated with the context of natural words should be listed. Here Ijust give a simplified example: The sentence,

[0162] “Association_‘bark of a dog’_—>_(——)

Noun_KW))

_=_(——)(bark

sound

)_,_(dog

animal

)_” is a sentence described in “sentence pattern of association” in asimplified form. The complete form will be shown later in the presentinvention.

3.1.3. Short Comment on ‘Classification Table’

[0163] In {circle over (∘)}“Roget's international thesaurus fourthedition” and/or in {circle over (∘)}“Roget's international thesaurusfifth edition”, not only ‘natural-nouns’ but also ‘natural-verbs’ aswell as categories are listed. In an ‘ideal thesaurus’ of anobject-oriented knowledge base system disclosed in the presentinvention, however, basically only ‘ideal nouns’ and/or ‘ideal nounphrases’ are listed as ‘descriptors’. In the present invention, verbsand/or verb phrases are recommended to be listed and classifiedsystematically as an entry of what I call ‘classification table’. Thus,a ‘name-of-classification-item’, which is an entry of a ‘classificationtable’ used in an ‘object-oriented knowledge base’ disclosed in thepresent invention, is basically a verb and/or a verb phrase.

[0164] The lexical definition of ‘classification table’ will be givenlater in the present invention. Here I show just an example: An exampleof such a ‘classification table’ comprising‘names-of-classification-items’ is shown in Formula. 6. A‘classification table’ in Formula. 6, is designed to be used as a tableof contents for a manual used as a guide of how to use an ideal Japaneseword processor. This ‘classification table’ contains verb phrases usedas a ‘name-of-classification-item’ such as

[0165] ‘to input half-sized alphanumeric characters’,

[0166] ‘to convert half-sized alphanumeric characters to hiraganacharacters in order to input hiragana characters’,

[0167] ‘to convert half-sized alphanumeric characters to kanjicharacters in order to input kanji characters’,

[0168] ‘to make a specification of the number of characters in a line’,

[0169] ‘to make a specification of the number of lines in a page’, and,

[0170] ‘to make a printer print a document on a paper’, etc.

[0171] These verb phrases are grouped and/or classified in a systematicway in the ‘classification table’.

[0172] Another example of an ‘classification table’ is shown in FIG. 23,in which dozens of verbs are classified in a systematic way. Detailedexplanation of FIG. 23 will be given later in the present invention.

3.1.3.1. ‘Ideal verbs’

[0173]

Lexical Definition of an ‘ideal verb’ and a ‘natural-verb’

In most cases, a verb used in a natural language (i.e. a ‘natural-verb’)is a polysemous verb and has several lexical meanings. I regard a verbphrase and/or a verb phrase a kind of verb in the present invention. Ifone uses a verb in an object-oriented knowledge base system disclosed inthe present invention, then, it is recommended that a lexical definitionof the verb should be given in an ‘ideal dictionary’ of the system. Ifand when a natural polysemous verb is defined in an ‘ideal dictionary’of the system, then, it is recommended that a name should be given toeach of its lexical meanings. I call such a name an ‘ideal verb’. By mydefinition, of course, an ‘ideal verb’ has only one lexical meaning.

[0174] It should be noted that many words used in English, including theword ‘abuse’ is used both as a noun and as a verb. For example, I havegiven an example in which I regard ‘abuse’ as a ‘natural-noun’ in thepresent invention, but I will show just below in the present invention,also a case in which I regard ‘abuse’ as a ‘natural-verb’. The‘natural-verb’, ‘abuse’ is a polysemous verb.

[0175] One of the lexical meanings of the polysemous verb ‘abuse’ is “todeliberately use something such as power or authority, for the wrongpurpose”. (See LONGMN dictionary of contemporary English) I name thislexical meaning ‘abuse

use

’. That is, ‘abuse

use

’=“to deliberately use something such as power or authority, for thewrong purpose”.

[0176] By my definition, ‘abuse

use

’ is what I call an ‘ideal verb’.

[0177] Another lexical meaning of the polysemous verb ‘abuse’ is “to sayrude or offensive things to someone”. And I name this lexical definition‘abuse

say

’. That is,

[0178] ‘abuse

say

’=“to say rude or offensive things to someone”.

[0179] By my definition, ‘abuse

say

’ is another example of what I call an ‘ideal verb’.

[0180] Here also, the word ‘ideal’ means ‘strict from the view point oflogic’.

[0181]

Lexical Definition of ‘ideal verb phrase’

I call a sentence and/or a phrase including a word used as an ‘idealverb’, an ‘ideal verb phrase’. I regard an ‘ideal verb phrase’ is a kindof an ‘ideal verb’.

3.1.3.2. ‘Ideal Verbs’ Used as ‘Names-of-Classification-Items’

[0182]

Lexical Definition of a ‘name-of-classification-item’

The name of an item listed in a ‘classification table’, is a‘name-of-classification-item’. It is recommended that, a ‘classificationtable’ disclosed in the present invention should be used to store theinformation about the classification of ‘ideal verbs’ and/or of ‘idealverb phrases’ used in an object-oriented knowledge base system disclosedin the present invention. Therefore, in most cases, a‘name-of-classification-item’ is an ‘ideal verb’ and/or an ‘ideal verbphrase’ used in the system.

5 3.1.3.3. ‘Ideal Verbs’ Used as the Names of an ‘Algorithm-of-Process’and a Sentence Described in “Sentence Pattern of Implementation of Namesof Algorithms-of-Processes” Lexical Definition of ‘Process’

[0183] A description of a ‘process’ consists of

[0184] a set of sentences and conjunctions arranged in a fixed order sothat to give a procedure having a function to cause a particular resultfrom a particular initial situation under a particular condition.

[0185] A sentence described in a natural language, an equation inmathematics, and/or, a function in computer programming languages may beused as such a sentence.

[0186] Sentences in natural languages that may be used as such asentence is classified as follows:

[0187] /*sentences describing situation*/

[0188] (1) describes the way in which something and/or someone begins tobe in a state,

[0189] (2) describes the state in which something and/or someone exists,

[0190] (3) describes the way in which state of something and/or someoneends,

[0191] (4) describes the way state of something and/or state of mind ofsomeone changes in an event,

[0192] (4-1) describes the way some conditions affects state ofsomething and/or state of mind of someone,

[0193] (4-2) describes the way some behavior effects state of somethingand/or state of mind of someone, and

[0194] /*sentences describing actions and/or reactions*/

[0195] /*(describing the way something and/or someone reacts to asituation and/or

[0196] *describes the way something and/or someone acts to change asituation).

[0197] */

[0198] (5) describes the way someone imagines an ideal situation in hismind,

[0199] (6) describes the way something and/or someone watches thesituation,

[0200] (7) describes the way someone makes an aim,

[0201] (8) describes the way someone discusses with others,

[0202] (9) describes the way someone thinks of a strategy and/or aprocedure,

[0203] 10) describes the way something and/or someone feels somethingand/or feels some event which happens,

[0204] (11) describes the way some emotion wells up and/or grows upand/or fades inside someone and/or inside something,

[0205] (12) describes the way something and/or someone behaves,

[0206] (12-1) describes the way someone commands,

[0207] (12-2) describes the way someone orders,

[0208] (12-3) describes the way someone controls,

[0209] (12-4) describes the way someone dose it himself,

[0210] (12-4-1) describes the way something operates according to theprocedure,

[0211] (12-4-2) describes the way someone follows his own mind and/orsomeone else's mind,

[0212] (13) describes the way someone judges whether to continue and/orto abandon,

[0213] (14) describes the way someone reconsiders,

[0214] (15) describes the way someone has effect,

[0215] (16) describes the way someone fails to have effect,

[0216] (17) describes the way someone brings the procedure naught,

[0217] (18) Ban-zai, Gyoku-sai. (an Honorable death.)

[0218] /*sentences describing the way something and/or someoneexperience a series of events

[0219] *and/or various conditions.

[0220] */

[0221] etc. etc.

[0222] Many other complex and complicated processes can be described bycomposing sentences using items of the classification listed above.

[0223] For example, how a scientific work is done can be described asfollows;

[0224] First, a scientist 7) tries to intend the aim of hisinvestigation. And then, a scientist 6) watches the state in whichsomething 2) exists and/or 6) the scientist watches the 4-1) the waysome conditions affects an event and/or 6) watches 4-2)way his actioneffects an event after 9)thinking of a strategy and/or a procedure forthe action, under a well defined conditions; that is, a scientistexperiments. Scientists 14) reconsider whether a knowledge of phenomenais right or wrong, by 6) watching right the result of the experiments,by 8) discussing with others scientists, and/or by 9) think andreasoning right. If the experiment fails, then, the scientist 13) judgesright whether he should continues the investigation or not. If continue,the scientist 14) reconsiders right the cause of failure, and plans thenext experiment. The aim of the scientists includes to 4) describes theway state of something and/or state of mind of someone changes as anevent, and to 2) describes the state in which something and/or someoneexists.

[0225]

Lexical Definition of ‘algorithm-of-process’

In the present invention, in ideal cases, an ‘algorithm-of-process’ is aprocedure, which describes the way to give a process to get a particularresult from a particular initial situation any time in any condition;that is, the ‘algorithm-of-process’ provides, at any time in anycondition, a ‘process’ which gives the particular result from theparticular initial situation.

[0226] In the contents of the ‘algorithm-of-process’, sentences andspecial conjunctions, such as ‘if’, ‘while’, ‘goto’, are categorized andarranged in a special manner

[0227] so that,

[0228] under the control of a computer and/or of someone, the ‘process’is assembled under the control of a previously established rule and auniversal grammar,

[0229] by linking one sentence after another sequentially to form achain of sentences, in some condition during the control,

[0230] by choosing a proper group of sentences and link it to theforegoing series of the sentences, according to the state in which acondition is during the control,

[0231] and/or,

[0232] by choosing the same group of sentences repeatedly and link themto the foregoing series of the sentences while the state is kept in aspecified condition during the control,

[0233] etc. etc.

[0234] By definition, an algorithm is a kind of an‘algorithm-of-process’.

[0235] By definition, functions and/or operators used in C languageincluding ‘assignment operator’, ‘=’, are as it is an‘algorithm-of-process’. And a computer program described in C languagecomposed by combining such functions and/or operators is also an‘algorithm-of-process’.

Lexical Definition of “sentence pattern of implementation of names ofalgorithms-of-processes”

[0236] “Sentence pattern of implementation of names ofalgorithms-of-processes” is a pattern of a sentence which is to be usedto implement the name of an ‘algorithms-of-processes’.

[0237] In the present invention, it is recommended that a quasi-C codedescribing the procedure of an ‘algorithm-of-process’ should be used asthe body of the ‘algorithms-of-processes’ in a sentence described in“sentence pattern of implementation of names ofalgorithms-of-processes”.

[0238] In a sentence described in “sentence pattern of implementation ofnames of algorithms-of-processes”, formalism including followingdescriptions are used;

[0239] 1) At the top of the sentence in “sentence pattern ofimplementation of names of algorithms-of-processes”, _ALGORITHM_is put.

[0240]2) After the _ALGORITHM_, the name of the ‘algorithms-of-process’is recommended to be described

[0241] 3) ‘{’ is used to show the starting point of the body of the‘algorithms-of-processes’.

[0242] 4) ‘}’ is used to show the ending point of a chain of sentences.

[0243] 5) ‘;_’ is used to show the end of a sentence.

[0244] 6) ‘if(**){****}’ is used to show a group of sentences, ‘****’,is chosen and is linked the foregoing series of the sentences, if thespecified condition, ‘**’, is satisfied.

[0245] 7) ‘while(***){*****}’ is used to show a group of sentences,‘****’, which I call a ‘loop body’, are chosen repeatedly and are linkedto the foregoing series of the sentences, while the specific condition,‘***’ is satisfied.

[0246] 8) At the end of the sentence described in “sentence pattern ofimplementation of names of algorithms-of-processes”, ‘}’ should bedescribed to show the ending point of the body of the‘algorithms-of-processes’.

[0247] For example, when the name of an ‘algorithm-of-process’, ‘****’is implemented by linking the name of an sub-‘algorithms-of-process’,‘**’, and the name of an sub-‘algorithms-of-process’, ‘***’,sequentially, then, the sentence in the “sentence pattern ofimplementation of names of algorithms-of-processes” describing ‘****’ isgiven by a simple quasi-C code, as,

[0248] _ALGORITHM_****{**;_***;_},

[0249] where, **** is the name of an ‘algorithm-of-process’ to beimplemented. And, either ‘**’ and/or ‘***’ represents the name of an‘algorithm-of-process’ used as a subroutine describing each step. Thebody of the ‘algorithm-of-process’ is “{**;_***;_}”.

[0250] The process assembled on the basis of this sentence described in”sentence pattern of implementation of names ofalgorithms-of-processes”, is always,

[0251] ****=**;_***;_, regardless of the condition, because either ‘if’and/or ‘while’, etc. is not used in the body of the‘algorithm-of-process’ in the sentence in the “sentence pattern ofimplementation of names of algorithms-of-processes”. Note that by mydefinition, sub-‘algorithms-of-process’ is a special kind of‘algorithm-of-process’. On the other hand, for example, if the name ofan ‘algorithm-of-process’, ‘*****’, is implemented by a sentencedescribed in the “sentence pattern of implementation of names ofalgorithms-of-processes”,

[0252] _ALGORITHM_*****{**;_if(today is Tuesday){***;_}*****;_},

[0253] then, the process assembled on the basis of this sentencedescribed in “sentence pattern of implementation of names ofalgorithms-of-processes”, is

[0254] *****=**;_***;_*****;_,

[0255] if today is Tuesday, and is

[0256] *****=**;_*****;_,

[0257] if today is not Tuesday.

[0258] For another example, ‘Euclidean algorithm’ is the name of an‘algorithm-of-process’ to obtain the greatest common divisor of twopositive integers.

[0259] And as still another example, ‘water diet’ ({circle over(∘)}“EARL MINDEL'S Vitamin Bible”0 p.344) is the name of an‘algorithm-of-process’ to lose weight by

[0260] drinking eight cups of water a day;_(—)

[0261] while having all-protein meals;_.

[0262] Note that sentence described in “sentence pattern ofimplementation of names of algorithms-of-processes” for ‘water diet’ is‘water diet’ { while(‘water diet’is continued) { drinking eight cups ofwater a day ;_(—) having all-protein meals ;_(—) } }.

[0263] Note that, here, ‘water diet’ is the name of the‘algorithms-of-processes’, and “{ while(‘water diet’ is continued) {drinking eight cups of water a day ;_(—) having all-protein meals ;_(—)} }”

[0264] is the body of the ‘algorithms-of-processes’.

Lexical Definition of ‘Means for Implementation of Names ofAlgorithms-of-Processes’

[0265] Sentence in “sentence pattern of implementation of names ofalgorithms-of-processes” and/or something that stores the information ofit, is a ‘means for implementation of names of algorithms-of-processes’.

[0266] Thus, the word ‘algorithm-of-process’ is used in a very widemeaning not only including strictly defined mathematical algorithm butalso including widely defined ones in the present invention. I claim inthe present invention that most verbs in natural languages are closelycorrelated to processes; detailed explanation of this issue will bedescribed later in the present invention, but I give just below a simpleexplanation by using some examples.

[0267] According to the {circle over (∘)}“Longman dictionary ofcontemporary English”, a verb is a word and/or group of words that isused to describe an action, experience, and/or state. In a word, many ofthe verbs describe an action. On the other hand, an action is theprocess of doing in order to deal with a problem and/or difficultsituation. In a word, an action is a kind of process. Therefore, in aword, many verbs and/or verb phrases denote a process.

[0268] For example, ‘to propose to a girl’ is asub-‘algorithm-of-process’; this sub-‘algorithm-of-process’ is a part ofthe total-‘algorithm-of-process’ acted by a guy to get married with agirl. As I regard a sub-‘algorithm-of-process’ as well as amain-‘algorithm-of-process’ is a kind of ‘algorithm-of-process’.

[0269] By the way, it is clear that if a guy wants to ‘propose to agirl’, then, it is necessary for him

[0270] ‘to make decision’;_,

[0271] and,

[0272] ‘to contact with a girl’;_.

[0273] What I claim here is that both ‘to make decision’ and ‘to contactwith a girl’ are a sub-‘algorithm-of-process’ which are a part of thetotal-‘algorithm-of-process’, i.e. ‘to propose to a girl’. In otherwords, strictly speaking, I regard the verb phrase, ‘propose to a girl’,as the name of the total-‘algorithm-of-process’ implemented by the twosub-‘algorithm-of-processes’. Note that the sentence described in“sentence pattern of implementation of names of algorithms-of-processes”for ‘propose to a girl’ is, ‘propose to a girl’ { ‘to make decision’;_(—) ‘to contact with a girl’ ;_(—) }.

[0274] Here, ‘propose to a girl’ is the name of the‘algorithm-of-process’, and “{ ‘to make decision’ ;_(—) ‘to contact witha girl’ ;_(—) }”,

[0275] is the body of the ‘algorithm-of-process’.

[0276] It will be shown later in the present invention that it is notalways necessary to describe in detail how the guy made a decision,and/or how the guy contacted a girl, to implement a verb phrase ‘A guyproposes to a girl’. That is, it is no importance whether a guy used aautomobile to meet the girl when he contacted the girl and/or the guyused a bicycle to meet the girl when he contacted her, to judge whetherthe guy proposed to the girl or not.

[0277] At the first place, little guy knows the detailed process bywhich he is charmed by a girl. The only thing that he knows is thatbefore he is charmed by a girl, he is not attracted by the girl, andafter he is charmed by the girl, he is attracted by the girl. He knowsonly the input and outcome of the process ‘being charmed by a girl’ whenhe knows that the girl charms him. In this sense, ‘being charmed by agirl’ is a black box to the guy. Only the input and the output of theblack box are visible to the guy. Love is blind.

[0278] In the same sense, ‘to make decision’ and/or ‘to contact with agirl’ may remain as a black box when the verb phrase ‘to propose to agirl’ is to be lexically defined.

[0279] As will be shown in the present invention, an object-orientedknowledge base system disclosed in the present invention can reasonusing a proposition “A guy proposes to a girl”, only if the lexicaldefinition of the words used in the proposition are given. The detailedmathematical definition is not necessarily a indispensable element. Iregard a proposition a kind of a verb phrase. That is, myobject-oriented knowledge base system can reason using a propositioneven when a lexical definition of a word used in it remains a black boxand the detailed mathematical definition of the word is not given. Thata black box may remain in a ‘knowledge’ is an important point to tell my‘algorithm-of-process’ from conventional and mathematical ‘algorithm’.The tool disclosed in the present invention to make such reasoning iswhat I call @[algorithm of sentence based object-oriented categoricalsyllogism]. The lexical definition of @[algorithm of sentence basedobject-oriented categorical syllogism] will be given later in thepresent invention.

3.1.3.3.1. C-Language-Like Way of Description of English Sentence byUsing “Sentence Pattern of Implementation of Names ofAlgorithms-of-Processes”

[0280] I claim in this invention that in ideal cases, a verb in anatural language can be regarded as a name of an ‘algorithm-of-process’.It is well known that, in most cases, a mathematical algorithm can bedescribed and/or outlined by using a quasi-C code. About the detail ofquasi-C code, see §3 of {circle over (∘)}“C gengo puroguramingu”.

[0281] Let me show here now just an example of a quasi-C code; Let usassume that the acceptance borderline of an examination for students ofa school is at the score, of 60. The quasi-C code,

[0282] “If the score of a student is greater than 60,

[0283] print ‘Pass’.”

[0284] prints the character ‘Pass’ when the condition “the score of astudent is greater than 60” is the truth for a student. When thiscondition is true, then the printer prints ‘Pass’.

[0285] If this sentence is translated into a sentence written in Clanguage, which is a kind of computer programming language, one gets if(grade >= 60 ) { printf(“Pass”); }

Lexical Definition of a Quasi-C Code

[0286] A quasi-C code is an artificial language written down using anatural language, such as, say, English, Japanese, Chinese, and/orGerman, etc. Usually, a quasi-C code is used by computer programmers asa tool that helps them to develop a code of a computer program, but theusage of quasi-C code is considerably object-oriented in the presentinvention. A quasi-C code well written by skillful programmers can betranslated just as it is into a code of a programming language, only bysimply replace the sentences written in the quasi-C code into a sentencewritten in the programming language.

[0287] A quasi-C code written in English is a C-language-like way ofdescription of English sentence.

[0288] In the present invention, I will disclose examples in which an‘algorithm-of-process’ giving the contents of the process defying an‘ideal verb’, is described and/or implemented using a quasi-C code.

[0289] In many cases, such black boxes as ‘to make decision’ and/or ‘tocontact with a girl’ are used as a function describing one of theinstructions in such a quasi-C code. (See later parts of the presentinvention about the detail).

[0290] For example, propose { make decision ;_(—) contact ;_(—) }.

[0291] In this example, ‘propose’ is the name of

[0292] the ‘algorithm-of-process’ whose body equals to “{ make decision;_(—) contact ;_(—) }”.

[0293] I claimed that in ideal cases, one lexical meaning of an ‘idealverb’ equals to ‘a category of one set of processes’. In a word, I claimthat in many ideal cases, an ‘ideal verb’ is the strict name of an‘algorithm-of-process’.

[0294] I give later in the present invention, more precise discussion inorder to show what I claim is reasonable and correct by implementingmany ‘ideal verbs’ by a quasi-C code. What I claim here is that it isrecommended to use a quasi-C code to give a lexical definition of a verbin an object-oriented knowledge base system disclosed in the presentinvention. More specifically speaking, I recommend to use a sentence inmy “sentence pattern of implementation of names ofalgorithms-of-processes” as a way to give a lexical definition of a verbin an object-oriented knowledge base system disclosed in the presentinvention. (See later parts in the present invention for the detail).

[0295] It should be noted that in a quasi-C code, sentences such as

[0296] A=B;_(—)

[0297] are often used. By analogy, I regard sentences describing a stateof something and/or someone such as

[0298] A is B;_(—)

[0299] as a kind of ‘algorithm-of-process’.

[0300] For example, a sentence,

[0301] Miss Amelia is beautiful;_(—)

[0302] is as a kind of a name of an ‘algorithm-o-process’.

[0303]

Lexical Definition of ‘simple sentence including a word used as an‘ideal verb’’

I regard a ‘simple sentence including a word used as an ‘ideal verb’’ asa kind of ‘ideal verb phrase’,

[0304] For example, sentences like

[0305] “Miss Amelia is beautiful;_”,

[0306] and/or,

[0307] “Miss Fumie is beautiful;_”

[0308] are regarded as a ‘simple sentence including a word used as an‘ideal verb’’, ‘be’.

[0309] It will be shown that, in most cases, it is difficult to give anexact and complete quasi-C code defying an ‘ideal verb’ without usingany ‘subject-word (S)’, ‘object-word (O)’, ‘complement-word (C)’,‘indirect-object-word (I.O)’, and/or ‘direct-object-word (D.O)’ of the‘ideal verb’. In a word, I think it difficult to define a word withoutusing a sentence. For the sake of simplicity, I regard in the presentinvention, a sentence a kind of a verb phrase.

[0310] That is, if a very precise description is necessary, it isrecommended to use a ‘simple sentence including a word used as an ‘idealverb’’ (i.e. to use a set comprising an ‘ideal verb’, and, a‘subject-word (S)’, and, an ‘object-word (O)’, an ‘indirect-object-word(I.O)’, a ‘direct-object-word (D.O)’, and/or a ‘complement-word (C)’,arranged according to the English grammar).

[0311] In the present invention, this ‘simple sentence including a wordused as an ‘ideal verb’’ is regarded as a precise ‘name’ of the‘algorithm of process’. It may seem strange to regard a sentence as a‘name’. A ‘name’ is of course a noun. But in grammar of German, asentence and/or a phrase is often compressed into a noun. Remember theGerman noun

[0312] ‘die Gesellschaftsgrüindung’,

[0313] whose meaning is ‘To establish a corporation’, for example. AGerman often expresses a ‘sentence’ in a compressed form as a noun. Inother words, it is recommended that an isolated single ‘ideal verb’should be used only as a simplified name of an ‘algorithm-of-process’.And it is recommended that as the full name of an‘algorithm-of-process’, a ‘simple sentence including a word used as the‘ideal verb’’ should be used.

[0314] For example, the lexical definition of ‘carry’ is ‘to lift andcarry something’. That is, ‘carry’ is implemented as, carry { someonelifts something ;_(—) someone takes something ;_(—) }

[0315] I regard “someone carries something” as a precise ‘full name’ ofan ‘algorithm-of-process’, “{ someone lifts something ;_(—) someonetakes something ;_(—) }”, and I regard ‘carry’ as a brief name of an‘algorithm-of-process’, “{ someone lifts something ;_(—) someone takessomething ;_(—) }”.

[0316] I regard a bare and isolated ‘ideal verb’ as the brief name of afunction of the ‘algorithm-of-process’, and I regard its ‘subject-word(S)’, ‘object-word (O)’, ‘indirect-object-word (I.O)’,‘direct-object-word (D.O)’, and/or ‘complement-word (C)’ of the ‘idealverb’ as ‘arguments’ of the function describing the‘algorithm-of-process’.

[0317] For example, I regard ‘carry’ as the name of a ‘function’describing the ‘algorithm-of-process’, and I regard ‘someone’ and‘something’ as the ‘arguments’ of the function.

[0318] Remember that in mathematics, a function is defined in a way, forexample,

f(x,y)=3x+y ²,

[0319] where, x and y are called an argument of the function ‘f( )’.What I say here is that I regard ‘carry’ as a function in the followingway, for example,

[0320] carry(‘someone’, ‘something)=“‘someone’ carries ‘something’”.

[0321] And this function, “carry(‘someone’, ‘something)” is implemented(and/or embodied) by a quasi-C code, as, carry(‘someone’, ‘something’) {lift(‘someone’, ‘something’) ;_(—) take(‘someone’, ‘something’) ;_(—) }.In this example, an ‘algorithm-of-process’, “{lift(‘someone’,‘something’) ;_(—) take(‘someone’,‘something’) ;_(—) }”

[0322] is assigned to

[0323] the function,

[0324] “carry(‘someone’, ‘something’)”.

Lexical Definition of Implementation

[0325] If and when an ‘algorithm-of-process’ which is denoted by afunction, is embodied by a quasi-C code used as the body of the‘algorithm-of-process’, then, I say that the function is implemented. Afunction is the name of an ‘algorithm-of-process’. The lexicaldefinition of ‘implementation’ is ‘to implement’ a function used as thename of an ‘algorithm-of-process’. It is recommended that a functionused as the name of an ‘algorithm-of-process’ should be implemented byusing “sentence pattern of implementation of names ofalgorithms-of-processes”. I regard a verb a kind of function used as thename of an ‘algorithm-of-process’.

[0326] If a ‘simple sentence including a word used as an ‘ideal verb’’is used to describe a lexical meaning a verb in an ‘ideal dictionary’,then, it is recommended to use as broader ‘descriptors’ as possible asthe ‘arguments’ used in the ‘simple sentence including a word used as an‘ideal verb’’. If broader ‘descriptors’ are used in ‘simple sentenceincluding a word used as an ‘ideal verb’’, then the power of expressionof the ‘simple sentence including a word used as an ‘ideal verb’’becomes more universal and more general. It is recommended thatuniversal and general explanation of the meaning of a word should beused in an ‘ideal dictionary’, because it covers very wide range ofcases.

[0327] For example, a sentence ‘someone carries something’ has moreuniversal and broader power of expression than ‘Mr. Bill carries a caseof beers’. Note here that, ‘someone’ is a broader descriptor of ‘Mr.Bill’. And ‘something’ is a broader descriptor of ‘a case of beers’.

[0328] If a formal description is desired, then, it is recommended touse what I call “sentence pattern of one of five basic sentence patternsof English grammar”. Such a formal sentence is readily used in reasoningby computer, by using my algorithms for reasoning in an object-orientedknowledge base system disclosed in the present invention, which will bedisclosed in detail later in the present invention.

[0329] Here, I give just an example: For example, if my “sentencepattern of one of five basic sentence patterns of English grammar”,which will be defined later in the present invention, is used,

[0330] the sentence, ‘someone carries something’

[0331] is represented as

[0332] SplusVplusO_(— —)S=_ someone _V=_ carry _O=_ something.

[0333] Details will be given later about the lexical definition of“sentence pattern of implementation of names ofalgorithms-of-processes”.

[0334] It should be noted that I regard a verb as a function, and a nounis a variable. A function in C language is described in a form,

[0335] A_Function(a variable, a variable, . . . , a variable);_(—)

[0336] In a word, all the variables are described in a bunch between apair of parentheses, ‘(’ and ‘)’.

[0337] By analogy of this, nouns in a ‘simple sentence including a wordused as an ‘ideal verb’’ may be described in a bunch between a pair ofparentheses, ‘(’ and ‘)’. In other words, descriptions such like

[0338] carry(‘someone’, ‘something);_(—)

[0339] may be used as a ‘simple sentence including a word used as an‘ideal verb’’.

[0340] In general, any number of sub-‘algorithms-of-processes’ may beinserted between ‘{’ and ‘}’ when total-‘algorithms-of-processes’ isdescribed in a quasi-C code. And symbols used in C language including“‘if( ){ }’ selection statements”, “‘for( ){ }’ iteration statements”,and/or “‘while( ){ }’ iteration statements” may be inserted between ‘{’and ‘}’ when total-‘algorithms-of-processes’ is described in a quasi-Ccode.

[0341] About the detail of quasi-C code, see for example §3 of {circleover (∘)}“C gengo puroguramingu”. Here, I give additional explanation insome detail; ‘In line implementation’ is allowed in a quasi-C code inthe present invention; That is, in a quasi-C code, nest of anotherquasi-C code may be used; For example, let us assume that if theimplementation of an algorithm whose name is PX is given by

[0342] _ALGORITHM_ PX {px1;_ px2;_ px3;_},

[0343] and, in addition, let us assume that the implementation of thealgorithm whose name is px2 is given by

[0344] _ALGORITHM_ px2 {px2_(—)1;_ px2_(—)2;_ px2_(—)3;_}.

[0345] I mean here that the latter quasi-C code can be nested into theformer quasi-C code, as,

[0346] _ALGORITHM_ PX {px1;_ px2 {px2_(—)1;_ px2_(—)2;_ px2_(—)3;_};_px3;_}.

[0347] This concept of ‘nesting’ is very common among the computerprogrammer using the C++ language, which is one of the most popularobject-oriented programming language today. Such concept of ‘nesting’ isnot common among C programmers. But such sentences including ‘nesting’can be easily implemented using C++ language, if a computer programmerdeclare the ‘function’ px2 as an ‘inline function’ using the key word‘inline’ in a source code written in C++ language.

[0348] In the present invention, I sometimes dare to confuse the usageof the term ‘algorithm’ and the term ‘function’. This analogy isreasonable. But strictly speaking, an ‘algorithm’ is the body of theprocedure with which to implement a ‘function’. In other words, a‘function’ is the name of an ‘algorithms-of-processes’.

[0349] In the present invention, I use not only a ‘function’ that iscompletely implemented by using an ‘algorithm’ but also a ‘function’that is not completely implemented by using an ‘algorithm’. I regard a‘function’ which is not completely implemented by using an ‘algorithm’ akind of black box. A ‘function’ that has no implementation is exactly agenuine ‘black box’.

[0350] In the present invention, I make use of a quasi-C code not onlyin descriptions of natural languages as well as in description ofcomputer programming languages. What I call ‘quasi-C code’ in thepresent invention include ‘quasi-C++ code’. A‘quasi-C code’ is a quasicode based on the C language, but the term ‘quasi-C code’ used in thepresent invention can be replaced without losing the generality by quasicodes based on other computer programming languages.

[0351] As further more concrete example, I express

[0352] ‘someone carries something’

[0353] in a very formal way as,

[0354] _ALGORITHM_ someone carries something {someone lifts something;_someone takes something;_}.

[0355] In this sentence described in “sentence pattern of implementationof names of algorithms, of-processes”, a ‘simple sentence including an‘ideal verb’’, “someone carries something” is implemented by using two‘simple sentences including an ‘ideal verb’’, “someone lifts something”and “someone takes something” as functions.

[0356] A bare and isolated ‘ideal verb’, ‘carry’ of course, may be putsolely as a function in a quasi-C code in the present invention, as,

[0357] _ALGORITHM_ carry {lift;_ take;_},

[0358] but detailed information is not described by this sentence.

[0359] If ‘someone carries something’ is to be expressed more C languagelike style, then, it is recommended that a ‘simple sentence includingthe ‘ideal verb’’ should be described in a quasi-C code's function likestyle, as,

[0360] _ALGORITHM_ carries(someone, something) {lifts(someone, something);_ takes(someone, something);_},

[0361] in which I emphasize that I regard a ‘simple sentence includingthe ‘ideal verb’’ as a kind of ‘function’. Here, the style,

[0362] “‘name of a function’‘(‘name of an argument, ‘name of anotherargument);_” is used.

[0363] This style of sentence is just equivalent to the style with whicha ‘function’ used in C language is defined (i.e. implemented) in asource code of C language. Once, this style is used to give a lexicaldefinition of a verb, ‘carry’, then, vast varieties of sentences can bedirectly defined using this style as a prototype, such like

[0364] _ALGORITHM_ carries(Tom, a case of beer) {lifts(Tom, a case ofbeer);_ takes(Tom, a case of beer);_},

[0365] _ALGORITHM_ carries(Alice, a case of orange juice) {lifts(Alice,a case of orange juice);_ takes(Alice, a case of orange juice);_},

[0366] etc. etc.

[0367] It should be noted that either Tom and/or Alice is narrower termof ‘someone’. And either ‘a case of beer’ and/or ‘a case of orangejuice’ is narrower term of ‘something’. It is recommended that if alexical definition of a verb (i.e. implementation of a verb) is given inan ‘ideal dictionary’ using a sentence described in “sentence pattern ofimplementation of names of algorithms-of-processes”, then as broader‘descriptors’ as possible should be used, to give a general anduniversal definition of the verb.

[0368] By the way, however, as a sentence, like,

[0369] carries (Tom, a case of beer),

[0370] is not necessarily readable for human. Therefore, I recommendthat one should use a style, just according to the English grammar torepresent exactly the same function, as,

[0371] (Tom) carries (a case of beer).

[0372] And if more formal description is desired, then, it isrecommended that the ‘simple sentence including the ‘ideal verb’’ shouldbe described in “sentence pattern of one of five basic sentence patternsof English grammar”. The lexical definition of “sentence pattern of oneof five basic sentence patterns of English grammar” will be given laterin the present invention.

3.1.3.3.2. Detailed Examples of Sentences in “Sentence Pattern of Namesof Implementation of Algorithms-of-Processes”, and “Sentence Pattern ofFunction” Used as a Lexical Meaning of an ‘Ideal Verb’

[0373] Data structure represented as “sentence pattern of implementationof names of algorithms-of-processes” and/or as what I call “sentencepattern of function” to give the lexical meanings of the verbs, ‘fall’,‘move’, ‘begin’, ‘end’, ‘change’, ‘draw’, ‘make’, ‘use’, ‘affect’,‘effect’, ‘imagine’, ‘watch’, ‘act’, ‘pronounce’, ‘answer’, ‘say’,‘ask’, ‘discuss’, ‘think’, ‘command’, ‘order’, ‘control’, ‘operate’,‘judge’, ‘arrive’, ‘depart’, ‘continue’, ‘break’, ‘bring’, ‘build’,‘lift’, ‘take’, ‘carry’, ‘chase’, ‘catch’, ‘walk’, will be shown below.

[0374] Here, let me discuss in some detail, about the relation betweenverbs used in English and a function in quasi-C code. To begin with, letme discuss the lexical meaning of the verb, ‘fall’. Remember thesentence, ‘A ripe apple fell off the tree.” In the {circle over(∘)}“Longman dictionary of contemporary English”, the verb ‘fall’ isdefined as “to move downwards from higher position to a lower position”.

[0375] Let me analyze this sentence: First, it should be noted firstthat ‘position’ is a ‘quality’ of something. In the present analysis, Idivide ‘position of something’ twofold into the ‘high position’ and the‘low position’. I regard ‘something’ as an ‘object’. It should be notedthat, this ‘dichotomy of ‘quality’’ makes the verb ‘fall’ moreanalytical and precise than the verb ‘move’. In other words, the meaningof the verb ‘fall’, how the ‘motion’ occurred is described moreanalytically and precisely using this dichotomy than the meaning of theverb ‘move’.

[0376] The verb ‘fall’ can be implemented (i.e. embodied) by using an‘algorithm-of-processes’ in a sentences what I call “sentence pattern ofimplementation of names of algorithms-of-processes”, as,

[0377] _ALGORITHM_ fall {position is high, at first;_ move;_ position islow, at last;_}.

[0378] On the other hand, the way in which the verb ‘fall’ works as afunction, can be described in a sentence described in what I call“sentence pattern of function”. The lexical definition of “sentencepattern of function” will be given later in the present invention. Here,I just give an example: If and when an ‘ideal verb’ is used as afunction in a quasi-C code, like in the present example, a sentence in“sentence pattern of function”, which will be defined later, is usefulto be used as a lexical definition of the verb, as follows:

[0379] _FUNCTION_ fall _translate_INPUT_ position is high, at first_into_OUTPUT_ position is low, at last.

[0380] Thus, a sentence in “sentence pattern of function” describes thesituation before and after the matter described by a verb happens. Thatis, a sentence in “sentence pattern of function” describes the input andthe output of the function. Here, sentences,

[0381] “position is high, at first”,

[0382] and

[0383] “position is low, at last”

[0384] are an example of sentences that stores the data describing theinformation about a ‘dichotomy of ‘quality’’.

[0385]

Lexical Definition of ‘dichotomy of ‘quality’’

A ‘dichotomy’ with which two completely opposite states of a ‘quality’is distinguished is a ‘dichotomy of ‘quality’’.

Lexical Definition of a Means for Making More Specific Meaning of a Verbfrom that of a Verb Whose Meaning is More General

[0386] The way in which sentences that stores the data describing theinformation about a ‘dichotomy of ‘quality’’ is added to the‘algorithms-of-processes’ for giving the lexical definition of the verbwhose meaning is more general,

[0387] and/or

[0388] something that stores the information of it,

[0389] is

[0390] a means for making more specific meaning of a verb from that of averb whose meaning is more general.

[0391] According to this definition and by the definition of what I call@[algorithm of giving definition of higher class ‘algorithm-of-process’and lower class ‘algorithm-of-process’], whose lexical definition willbe given later in the present invention,

[0392] if and when

[0393] ‘algorithm of process’ which is used to give a lexical definitionof a verb having a specific meaning (for example, “{position is high, atfirst;_ move;_ position is low, at last;_}”)

[0394] is obtained by adding

[0395] a sentences that stores the data describing the information abouta ‘dichotomy of ‘quality’’ (for example, “position is high, at first;_”and “position is low, at last;_”) to

[0396] the ‘algorithms-of-processes’ which is used to give a lexicaldefinition of a verb having a general and universal meaning (forexample, “{move;_}”),

[0397] then,

[0398] the general ‘algorithms-of-processes’ should be regarded as thehigher class ‘algorithm-of-process’ of the specific‘algorithms-of-processes’. In other words, I regard the verb ‘move’ asthe higher class verb of ‘fall’ (See FIG. 23). ‘This situation isschematically shown in FIG. 4, which shows that ‘means for givingdefinition of higher class algorithm-of-process and lower classalgorithm-of-process’ must be used if and when ‘means for making morespecific meaning of a verb from that of a verb whose meaning is moregeneral’ is carried out.

[0399] I regard, in the present invention, a storing media on which theshape and/or pattern of characters is made so that information isrecorded, as a kind of sentence. For example, if a man composes asentence on a paper with a pencil, the sentence is recorded as the shapeof the lead line deposited on the paper by using the pencil. In thiscase, I regard the paper on which the lead line having a form ofcharacters, as a storing media on which the shape of characters is madeso that information is recorded. By definition, I regard the paper onwhich the lead line having a form of characters, as a kind of sentence.For example, a sentence recorded on a Hard disk is expressed as thepattern of arrangement of N-S direction of the micro domains of magnetson the Hard disk, which is regarded as a kind of 0-1 characters,recorded on the thin film magnetic media on a hard Aluminum disk and/oron a hard glass disk. Therefore in this case, I regard a magnetic diskthat has a magnetic pattern on the surface of it, as a sentence.

[0400] Let me show another example. I regard a computer memory whoseelectric state is made so that information is recorded as a kind ofsentence. For example, a sentence recorded in a dynamic random accessmemory (DRAM) is expressed as the pattern of arrangement of chargedand/or uncharged micro condensers embedded and arranged in theintegrated circuit of the DRAM. Thus, in this case, I regard a patternof arrangement of charged and/or uncharged micro condensers embedded inthe DRAM as an electric state of a DRAM. In other words, in this case, Iregard a DRAM, which has an electric pattern therein, as a sentence.

[0401] Various application specific integrated circuit also has microelectric patterns in it, and I regard a specific integrated circuithaving micro electric patterns as a sentence.

[0402] Thus, I regard in the present invention that a sentence is a kindof an material article as well as a kind of information. Accordingly, Iregard the contents of a knowledge base system as a kind of article aswell as a kind of information.

[0403] If and when precise description is necessary, it is recommendedto describe a verb as a function used as the name of an‘algorithm-of-process’ of the verb, by using ‘simple sentence includinga word used as an ‘ideal verb’’ instead of using a bare and isolated‘ideal verb’: For example, in the sentence, which gives a precisedescription of the mplementation (=embodiment of the detail) of the verb‘fall’,

[0404] _ALGORITHM_ (something) falls {(something) has position which isjudge to and/or is felt to be high, at first;_ (something) moves;_ (something).has position which is judged and/or is felt to low, at last;_}},

[0405] wherein

[0406] the name of the ‘algorithm-of-process’ is described as,

[0407] “(something) falls”,

[0408] which is what I call a function, described as a ‘simple sentenceincluding a word used as an ‘ideal verb’’,

[0409] and the body of the ‘algorithm-of-process’ is described as aquasi-C code,

[0410] “{(something) has position which is judge to and/or is felt to behigh, at first;_ (something) moves;_ (something).has position which isjudged and/or is felt to low, at last;_}}”.

[0411] It is clear in this example that the verb ‘move‘is used toimplement (=to embody the detail of) the verb ‘fall’. In this sense,‘move’ is a more primitive verb than ‘fall’. I describe this situationthat

[0412] “‘move’ is higher class ‘algorithm-of-process’ of ‘fall’.”

[0413] I also describe this situation in a more formal way, as,

[0414] _ALGORITHM_ (something) moves _is_higher_class_of_ALGORITHM_(something) falls.

[0415] And

[0416] “‘have’ is higher class ‘algorithm-of-process’ of ‘fall’.”

[0417] _ALGORITHM_ (something) has position that is judge to and/or isfelt to be high, at first _is_higher_class_of_ALGORITHM_ (something)falls.

[0418] _ALGORITHM_ (something).has position that is judged and/or isfelt to low, at last _is_higher_class_of_ALGORITHM_ (something) falls.

[0419] The lexical definition of the word “higher class‘algorithm-of-process’” will be given later in the present invention. Inthe present invention, I regard the verb ‘fall’ as a function that mapsthe initial situation into the final situation. Describe this ideaexplicitly, I give a sentence,

[0420] _FUNCTION_ (something) falls;_(— —)translate_INPUT_(something).has position which is judge to be high, atfirst;_(— —)into_OUTPUT_ (something).has position which is judged to beand/or is felt to be high low, at last;_,

[0421] where, I regard ‘something’ as an ‘object’. And I regard‘position’ as what I call an ‘‘individual variable’ of the ‘object’’,which is a ‘quality’ characterizing the ‘object’. And I regard ‘high’and ‘low’ as a value of the ‘individual variable’. As a matter of fact,for example, the ‘simple sentence including a word used as an ‘idealverb’’,

[0422] ‘(something) has position which is judge to be high, at first’

[0423] can be translated into a sentence described in what I call a“Sentence pattern of definition of object”, as

[0424] _OBJECT_ (something) have_VARIABLES (position) which_is judged tobe and/or felt to be high, at first.

[0425] The same sentence can be translated in a sentence using the‘assignment operator’, ‘=’, which a C programmer often uses, as

[0426] something.state=high;_.

[0427] (at first)

[0428] Here, the general and universal lexical definition of‘(something) moves’, in which

[0429] ‘move’ is regarded as a black box, is given by

[0430] _FUNCTION_ (something) moves;_(— —)translate_INPUT_ (something)has (position) which is judged to be and/or felt to be at some state, atfirst;_(— —)into_OUTPUT_ (something) has (position) which is judged tobe and/or felt to be at the other state, at last;_;_

[0431] The word, ‘high’, is an adjective that represents the state.

[0432] It should be noted that most of the physicists loves to addquantitative expression to a ‘quality’. For example, they tend to say“It is five meters high from the ground” instead simply saying “It ishigh from the ground”.

[0433] I show here an example in which ‘quantity’ is used as a ‘value’of an ‘quality’, which is used an ‘individual variable’:

[0434] “‘Position of something’ is five feet from the ground, at first,”

[0435] where, ‘something’ is an object, ‘position’ is a ‘quality’ of theobject, ‘five’ is a number, ‘the ground’ is the origin from which the‘quality’ is measured, and ‘foot’ is something to determine the unit oflength. Thus, ‘five feet from the ground’ is the ‘quantity’ used as thevalue of the ‘quality’.

[0436] The verb ‘move’ recited above is used as an intransitive verb.Let me discuss the lexical meaning of the verb, ‘move’ which is used asa transitive verb.

[0437] It is used in sentences such as, “Can you move your wagon? It isblocking the road” In the {circle over (∘)}“Longman dictionary ofcontemporary English”, the verb ‘move’ is defined as “to make somethingchange its place.”.

[0438] First, I interpret this definition that, ‘position of something’is divided twofold into the ‘in initial place’, and the ‘in finalposition’. This ‘dichotomy of ‘quality’’ makes the verb ‘move’ moreanalytical and precise than the verb ‘perform something’. In otherwords, the meaning of the verb ‘move’ includes how ‘to perform aprocess’ in a form of the ‘dichotomy of ‘quality’’. Note that the twosentences,

[0439] (something) has position which is judge to and/or is felt to behigh, at first;_ and

[0440] something).has position that is judged and/or is felt to low, atlast;_, are sentences expressing the ‘dichotomy of ‘quality’’.

[0441] ‘Algorithm-of-processes’ of the verb ‘move’ can be describedusing a quasi-C code. Lexical definition of ‘move’ is given by using my“sentence pattern of implementation of names of algorithms-of-processes”and “Sentence pattern of function”, as follows;

[0442] _ALGORITHM_ (someone) moves (something) {(something) has positionwhich is udged to be and/or felt to be ‘in initial place’, at first;_(someone) performs a (process);_ (something) has position which isjudged to be and/or felt to be ‘in final place’, at last;_}.

[0443] _FUNCTION_ (someone) moves (something);_(— —)translate_INPUT_(something) has position which is judged to be and/or felt to be ‘ininitial place’, at first;_(— —)into_OUTPUT_ (something) has positionwhich is judged to be and/or felt to be ‘in final place’, at last;_;_,

[0444] It should be noted that the verb ‘perform’ is used to implementthe lexical meaning of the verb ‘move’. Therefore, the verb ‘perform’ ismore fundamental verb than the verb ‘move’. And as I noted before, Iregard a ‘verb’ as a name of an ‘algorithm-of-process’. By mydefinition, which will be given later in the present invention, thissituation is described as,

[0445] “‘perform’ is higher class ‘algorithm-of-process’ of ‘move’(intransitive).”

[0446] More precisely describing,

[0447] _ALGORITHM_ (someone) performs a (process)_is_higher_class_of_ALGORITHM_ (someone) moves (something).

[0448] And

[0449] “‘have’ is higher class ‘algorithm-of-process’ of ‘move’(intransitive).”

[0450] More precisely describing,

[0451] _ALGORITHM_ (something) has position which is judged to be and/orfelt to be ‘in initial place’, at first _is_higher_class_of_ALGORITHM_(someone) moves (something).

[0452] _ALGORITHM_ (something) has position that is judged to be and/orfelt to be ‘in final place’, at last _is_higher_class_of_ALGORITHM_(someone) moves (something).

[0453] As another example, let me discuss the lexical meaning of theverb, ‘begin’. It is used in sentences such as, “We began to wonder ifthe train would ever arrive.” In the {circle over (∘)}“Longmandictionary of contemporary English”, the verb ‘begin’ is defined as “tostart doing (something).”. First, I interpret that in this definition,‘state of process’, which I regard a kind of ‘quality’, is dividedtwofold into the ‘in motion’, and the ‘out of motion’. In this sense,this ‘dichotomy of ‘quality’’ makes the verb ‘begin’ more analytical andprecise than the verb ‘do’. In other words, the meaning of the verb‘begin’ contains the information of how ‘to do a process’ in a form ofthe ‘dichotomy of ‘quality’’.

[0454] ‘Algorithm-of-processes’ describing the lexical meaning of theverb ‘start’ can be described using a quasi-C code. Lexical definitionof ‘start’ is given by using my “sentence pattern of implementation ofnames of algorithms-of-processes” and “Sentence pattern of function” asfollows;

[0455] _ALGORITHM_ (someone) begins a (process) {state of (process) isjudged to be and/or felt to be ‘out of motion’, at first;_ while(){(someone) dose (process);_ if(state of (process) is judged to beand/or felt to be ‘in motion’){break;_}}},

[0456] in which the verb ‘do’ is used to implement the lexical meaningof the verb ‘begin’. And the sentence, while( ){ (someone) dose(process);_ if (state of (process) is judged to be and/or felt to be ‘inmotion’) { break ;_} }

[0457] means

[0458] that (someone) continues to do (process) repeatedly, but that ifthe condition “state of (process) is judged to be and/or felt to be ‘inmotion’” is satisfied, then, break the continuation, and (someone) stopsto do the (process).

[0459] _FUNCTION_ (someone) begins a (process);_(— —)translate_INPUT_(process) has state which is judged to be and/or felt to be out ofmotion, at first;_(— —)into_OUTPUT_ (process) has state which is judgedto be and/or felt to be in motion, at last;_;_,

[0460] It is clear, by my definition, that,

[0461] “‘do’ is higher class ‘algorithm-of-process’ of ‘begin’.”

[0462] In other words,

[0463] _ALGORITHM_ (someone) dose (process)_is_higher_class_of_ALGORITHM_ (someone) begins a (process).

[0464] And

[0465] “if(state of (process) is judged to be and/or felt to be ‘inmotion’)

[0466] {break;_}”

[0467] is higher class ‘algorithm-of-process’ of ‘begin’.

[0468] As another example, let me discuss the lexical meaning of theverb, ‘end’. It is used in sentences such as, “The fighting ended atlast.” In the {circle over (∘)}“Longman dictionary of contemporaryEnglish”, the verb ‘end’ is defined as “to finish or to stop.”.

[0469] First, I interpret this definition that, ‘state of (process)’ isdivided twofold into the ‘in motion’, and the ‘out of motion’. In thissense, this ‘dichotomy of ‘quality’’ makes the verb ‘end’ moreanalytical and precise than the verb ‘do’. In other words, the meaningof the verb ‘end’, contains the information of how ‘to do a (process)’is described in a form of the ‘dichotomy of ‘quality’’.

[0470] ‘Algorithm-of-processes’ of the verb ‘end’ can be described usinga quasi-C code. The lexical definition of ‘end’ is given by using my“sentence pattern of implementation of names of algorithms-of-processes”and “Sentence pattern of function” as follows;

[0471] _ALGORITHM_ (someone) ends (process) {state of (process) isjudged to be and/or felt to be ‘in motion’, at first;_ state of(process) is judged to be and/or felt to be ‘out of motion’;_},

[0472] in which the verb ‘do’ is used to implement the lexical meaningof the verb ‘end’.

[0473] _FUNCTION_ (someone) ends (process);_(— —)translate_INPUT_(process) has state which is judged to be and/or felt to be in motion,at first;_(— —)into_OUTPUT_ (process) has state which is judged to beand/or felt to be out of motion, at last;_;_,

[0474] It is clear, by my definition, that,

[0475] “‘do’ is higher class ‘algorithm-of-process’ of ‘end’.”

[0476] In other words,

[0477] _ALGORITHM_ (someone) dose (process)_is_higher_class_of_ALGORITHM_ (someone) ends (process).

[0478] And “‘be’ is higher class ‘algorithm-of-process’ of ‘end’.”

[0479] In other words,

[0480] _ALGORITHM_ state of (process) is judged to be and/or felt to be‘in motion’ _is_higher_class_of_ALGORITHM_ (someone) ends (process).

[0481] _ALGORITHM_ state of (process) is judged to be and/or felt to be‘out of motion’ _is_higher_class_of_ALGORITHM_ (someone) ends (process).

[0482] As another example, let me discuss the lexical meaning of theverb, ‘change’. First, I divide ‘state of something’ twofold into the‘initial’, and the ‘final’. ‘Algorithm-of-processes’ of the verb can bedescribed using a quasi-C code. The lexical definition of ‘change’ isgiven by using my “Sentence pattern of function” can be used as follows;

[0483] _FUNCTION_ (someone and/or something)changes;_(— —)translate_INPUT_ (someone and/or something) has statewhich is judged to be and/or felt to be initial, atfirst;_(— —)into_OUTPUT_ (someone and/or something) has state which isjudged to be and/or felt to be in final, at last;_;_,

[0484] As another example, let me discuss the lexical meaning of theverb, ‘make’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘make’ is given by usingmy “sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0485] _FUNCTION_ (someone) makes (something);_(— —)translate_INPUT_image of an object has state which is judged and/or is felt to exist in(someone)’ mind;_(— —)into_OUTPUT_ image of the object has state whichis judged and/or is felt to exist as shape of material substance;_;_(—)

[0486] _ALGORITHM_ (someone) makes (something) {state of (something) isjudged to be and/or felt to be exist in (someone)’ mind;_ (someone)performs a (process);_ state of (something) is judged to be and/or feltto be exist as shape of material substance;_}.

[0487] It is clear, by my definition, that,

[0488] “‘perform’ is higher class ‘algorithm-of-process’ of ‘make’.”

[0489] In other words,

[0490] _ALGORITHM_ (someone) performs a (process)_is_higher_class_of_ALGORITHM_ (someone) makes (something).

[0491] And, ‘be’ is higher class ‘algorithm-of-process’ of ‘make’.

[0492] In other words,

[0493] _ALGORITHM_ state of (something) is judged to be and/or felt tobe exist in (someone)’ mind _is_higher_class_of _ALGORITHM_ (someone)makes (something).

[0494] _ALGORITHM_ state of (something) is judged to be and/or felt tobe exist as shape of material substance _is_higher_class_of_ALGORITHM_(someone) makes (something).

[0495] As another example, let me discuss the lexical meaning of theverb, ‘use’. ‘Algorithm-of-processes’ of the verb can be described usinga quasi-C code. The lexical definition of ‘use’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of function”

[0496] _FUNCTION_ (someone) uses (something); _(— —)translate_INPUT_ideas of some procedure have state which is judged and/or is felt toexist in (someone)’ mind, at first;_(— —)into_OUTPUT_ ideas and/orimages of some procedure have a state which is judged and/or is felt toexist as (someone)'s work helped by (something)'s work with a shape madeof material substance;_;_(—)

[0497] _ALGORITHM_ (someone) uses (something) {idea of some procedurehave state which is judged and/or is felt to exist in (someone)’ mind;_(someone) performs a (process);_ ideas and/or images of some procedurehave a state which is judged and/or is felt to exist as (someone)'s workhelped by (something)'s work with a shape made of materialsubstance;_}.,

[0498] in which the verb ‘perform’ is used to implement the lexicalmeaning of the verb ‘use’.

[0499] It is clear, by my definition, that,

[0500] “‘perform’ is higher class ‘algorithm-of-process’ of ‘use’.”

[0501] In other words,

[0502] _ALGORITHM_ (someone) performs a (process)_is_higher_class_of_ALGORITHM_ (someone) uses (something).

[0503] As another example, let me discuss the lexical meaning of theverb, ‘draw’. It is used in sentences such as, “Can I draw yourportrait?”. In the {circle over (∘)}“Longman dictionary of contemporaryEnglish”, the verb ‘draw’ is defined as “to make picture of somethingwith pencil and/or pen”.

[0504] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘draw’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of function”, and, and“sentence pattern of ‘ideal thesaurus’” as, _ALGORITHM_ (someone) draws(picture) { while( ){ (someone) makes (picture) { something = penciland/or pen and/or a brush ;_ (someone)'s work = (someone) makes a formand/or a pattern of deposited pigment on a paper and/or on a screen ;_(someone) uses (something) ;_ } } },

[0505] _FUNCTION_ (someone) draws (picture);_(— —)translate_INPUT_ imageof an object has state which is judged to be and/or felt to be exist in(someone)'s mind, at first;_(— —)into_OUTPUT_ image of the object hasstate which is judged to be and/or felt to be exist as dots lines,and/or planes on papers and/or on screens, at last;_;_,

[0506] It should be noted that

[0507] if

[0508] image of object has state that is judged and/or is felt to existas dots, lines, and/or planes on papers and/or on screens,

[0509] then,

[0510] image of object has state that is judged and/or is felt to existas shape of material substance.

[0511] Therefore, the lexical definition of the verb ‘make’ can beusedas it is in the lexical definition of the verb ‘draw’.

[0512] _NT_ pencil and/or pen _is_a_kind_of_BT_ something.

[0513] _ALGORITHM_ (someone) make (picture)_is_higher_class_of_ALGORITHM_ (someone) draw (picture).

[0514] _ALGORITHM_ (someone) use pencil and/or pen)_is_higher_class_of_ALGORITHM_ (someone) draw (picture).

[0515] As another example, let me discuss the lexical meaning of theverb, ‘affect’. ‘Algorithm-of-processes’ of the verb ‘affect’ can bedescribed using a quasi-C code. The lexical definition of ‘affect’ isgiven by using my “sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of function”.

[0516] _FUNCTION_ (something) affects (somethingelse);_(— —)translate_INPUT_ (something else) has state that is judgedto be and/or felt to be in initial, at first;_(— —)into_OUTPUT_(something else) has state that is judged to be and/or felt to be infinal according to (some condition), at last;_;_,

[0517] _ALGORITHM_ (something) affects (something else);_ {(somethingelse) has state which is judged to be and/or felt to be in initial, atfirst;_ (something) performs a (process);_ (something else) has statewhich is judged to be and/or felt to be in final according to (somecondition);_}.

[0518] It is clear, by my definition, that,

[0519] “‘perform’ is higher class ‘algorithm-of-process’ of ‘affect’.”

[0520] In other words,

[0521] _ALGORITHM_ (something) performs a (process)_is_higher_class_of_ALGORITHM_ (something) affects (something else).

[0522] And

[0523] “‘have’ is higher class ‘algorithm-of-process’ of ‘affect’.”

[0524] In other words,

[0525] _ALGORITHM_ (something else) has state that is judged to beand/or felt to be in initial, at first _is_higher_class_of_ALGORITHM_(something) affects (something else).

[0526] _ALGORITHM_ (something else) has state that is judged to beand/or felt to be in final according to (some condition)_is_higher_class_of_ALGORITHM_ (something) affects (something else).

[0527] As another example, let me discuss the lexical meaning of theverb, ‘effect’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘effect’ is given byusing my “Sentence pattern of function” as follows;

[0528] _FUNCTION_ (some behavior) effects(something);_(— —)translate_INPUT_ (something) has state which is judgedto be and/or felt to be initial, at first;_(— —)into_OUTPUT_ (something)has state which is judged to be and/or felt to be in final according tothe (some behavior), at last ;_;_,

[0529] As another example, let me discuss the lexical meaning of theverb, ‘imagine’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘imagine’ is given byusing my “sentence pattern of function” as,

[0530] _FUNCTION_ (someone) imagines (picture);_(— —)translate_INPUT_(picture) in (someone)'s mind is judged and/or is felt to benonexistent, at first;_(— —)into_OUTPUT_ (picture) in (someone)'s mindis judged and/or is felt to be and/or felt to be exist, at last;_;_,

[0531] _ALGORITHM_ (someone) imagines (picture);_ {(picture) in(someone)'s mind is judged and/or is felt to be nonexistent;_ (someone)performs a (process);_ (picture) in (someone)'s mind is judged and/or isfelt to be and/or felt to be exist, at last;_}.

[0532] It is clear, by my definition, that,

[0533] “‘perform’ is higher class ‘algorithm-of-process’ of ‘imagine’.”

[0534] In other words,

[0535] _ALGORITHM_ (someone) performs a (process)_is_higher_class_of_ALGORITHM_ (someone) imagines (picture).

[0536] And

[0537] “‘be’ is higher class ‘algorithm-of-process’ of ‘imagine’.”

[0538] _ALGORITHM_ (picture) in (someone)'s mind is judged and/or isfelt to be nonexistent, at first _is_higher_class_of_ALGORITHM_(someone) imagines (picture).

[0539] _ALGORITHM_ (picture) in (someone)'s mind is judged and/or isfelt to be and/or felt to be exist, at last_is_higher_class_of_ALGORITHM_ (someone) imagines (picture).

[0540] As another example, let me discuss the lexical meaning of theverb, ‘watch’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘watch’ is given byusing my “sentence pattern of function” as,

[0541] _FUNCTION_ (someone) watches (something);_(— —)translate_INPUT_some objects have a state which is judged and/or is felt to exist as(something) with a shape made of material substance, atfirst;_(— —)into_OUTPUT_ (ideas and/or images) of some objects have astate which is judged and/or is felt to exist in (someone)’ mind, atlast;_;_,

[0542] _ALGORITHM_ (someone) watches (something);_ {some objects have astate which is judged and/or is felt to exist as (something) with ashape made of material substance, at first ;_ (someone) performs a(process);_ (ideas and/or images) of some objects have a state which isjudged and/or is felt to exist in (someone)’ mind, at last;_}.

[0543] It is clear, by my definition, that,

[0544] “‘perform’ is higher class ‘algorithm-of-process’ of ‘watch’.”

[0545] In other words,

[0546] _ALGORITHM_ (someone) performs a (process)_is_higher_class_of_ALGORITHM_ (someone) watches (something).

[0547] And

[0548] “‘have’ is higher class ‘algorithm-of-process’ of ‘watch’.”

[0549] _ALGORITHM_ some objects have a state that is judged and/or isfelt to exist as (something) with a shape made of material substance, atfirst _is_higher_class_of_ALGORITHM_ (someone) watches (something).

[0550] _ALGORITHM_ (ideas and/or images) of some objects have a statewhich is judged and/or is felt to exist in (someone)’ mind, at last_is_higher_class_of_ALGORITHM_ (someone) watches (something).

[0551] As another example, let me discuss the lexical meaning of theverb, ‘act’. It is used in sentences such as, “The UN Security Councilacted to end the war in Bosnia.”

[0552] I define the verb ‘act’ as

[0553] “to do something as a measure to realize a situation which inmost cases you hope and/or you judge strategically important.”

[0554] First, I interpret this definition that, ‘state of measure’ isdivided twofold into the ‘to have been performed’ and the ‘not to havebeen performed’. In this sense, this ‘dichotomy of ‘quality’’ makes theverb ‘act’ more analytical and precise than the verb ‘perform’. In otherwords, the meaning of the verb ‘act’, contains the information of howthe ‘process of performance’ occurred is described in a form of‘dichotomy of ‘quality’’.

[0555] ‘Algorithm-of-processes’ of the verb ‘act’ can be described usinga quasi-C code. The implementation of the verb ‘act’ is given by usingmy “sentence pattern of implementation of names ofalgorithms-of-processes”, as, _ALGORITHM_ (someone) acts { if( (someone)acts strategically ) { (someone) intends the (aim) ;_ (someone else)tries to make the (strategy) ;_ } else if( (someone) is driven by hisemotion ) { (someone) feels (emotion) ;_ } while( ) { if( (someone) actsstrategically ) { (another one) tries to make (the procedure) accordingto (the strategy) ;_ if( (ideal situation) is actually realized, and/or{ break ;_ } } else if ((someone) is driven by his emotion) { if((someone)'s emotion changes ){ break ;_} } (still another one) dares toperform (something) ;_ } }.

[0556] _FUNCTION_ (someone) acts;_(— —)translate_INPUT_ measure hasstate which is judged to be and/or felt to be not to have beenperformed, at first;_(— —)into_OUTPUT_ measure has state which is judgedto be and/or felt to be to have been performed, at last;_;_,

[0557] It is clear, by my definition, that,

[0558] “‘do’ is higher class ‘algorithm-of-process’ of ‘act’.”

[0559] In other words,

[0560] _ALGORITHM_ (someone) dares to perform(something);_(— —)is_higher_class_of_ALGORITHM_ (someone) acts;_;_. And“if( (someone) acts strategically ) { (someone) intends the (aim) ;_(someone else) tries to make the (strategy) ;_ }” is higher class‘algorithm-of-process’ of ‘act’. And “else if( (someone) is driven byhis emotion ) { (someone) feels (emotion) ;_ }” is higher class‘algorithm-of-process’ of ‘act’. And “if( (someone) acts strategically ){ (another one) tries to make (the procedure) according to (thestrategy) ;_ if( (ideal situation) is actually realized, and/orstrategic necessity ceases to exist ) { break ;_ } } else if ((someone)is driven by his emotion) { if( (someone)'s emotion changes ){ break ;_}}” is higher class ‘algorithm-of-process’ of ‘act’. “(still another one)dares to perform (something) ;_” is higher class ‘algorithm-of-process’of ‘act’.

[0561] ‘Act’ is what I call a ‘compound verb’, whose lexical definitionwill be given later in the present invention.

[0562] As another example, let me discuss the lexical meaning of theverb, ‘pronounce’. It is used in sentences such as, “How do youpronounce your name?” In the {circle over (∘)}“Longman dictionary ofcontemporary English”, the verb ‘pronounce’ is defined as

[0563] “to make the sound of a word”.

[0564] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘pronounce’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of function” and “sentencepattern of ‘ideal thesaurus’”, as,

[0565] _ALGORITHM_ (someone) pronounces word {word has state which isjudged and/or is felt to exist in (someone)'s mind;_ while( ){(someone)makes word sound;_ if(word has state which is judged and/or is felt toexist as a sound in the air){break;_}}}

[0566] and

[0567] _FUNCTION_ (someone) pronounces word;_(— —)translate_INPUT_ wordhas state which is judged and/or is felt to exist in (someone)'smind;_(— —)into_OUTPUT_ word has state which is judged and/or is felt toexist as sound in the air;_;_,

[0568] where,

[0569] _NT_ word _is_a_kind_of_BT_ image of an object,

[0570] _NT_ sound in air _is_a_kind_of_BT_ shape of material substance,

[0571] and

[0572] _ALGORITHM_ make _is_higher_class_of_ALGORITHM_ pronounce.

[0573] _ALGORITHM_ have _is_higher_class_of_ALGORITHM_ pronounce.

[0574] And

[0575] “if(word has state which is judged and/or is felt to exist as asound in the air){break;_}”

[0576] is higher class ‘algorithm-of-process’ of ‘act’.

[0577] As another example, let me discuss the lexical meaning of theverb, ‘answer’. It is used in sentences such as, “I had to answer lotsof questions about my childhood.”

[0578] In the {circle over (∘)}“Longman dictionary of contemporaryEnglish”, the verb ‘answer’ is defined as

[0579] “to say something to someone as a reply when they asked you aquestion”.

[0580] First, I interpret this definition that, ‘word’ is dividedtwofold into ‘answer’ and ‘question’. In this sense, this ‘dichotomy of‘quality’’ makes the verb ‘answer’ more analytical and precise than theverb ‘say’. In other words, the meaning of the verb ‘answer’, how the‘conversation’ occurred is described using the ‘dichotomy of ‘quality’’.

[0581] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘answer’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of function” and “sentencepattern of ‘ideal thesaurus’”, as, _ALGORITHM_ (someone) answers(someone else)'s (question) { another one = someone ;_ ideal situation =(someone else) gets the necessary information ;_ still another one =someone ;_ to perform (something) = to say (something) ;_ (someone) acts;_ }.

[0582] _FUNCTION_ (someone) answers (someone else) a(question);_(— —)translate_INPUT_ (someone)'s reply to (someone else)'s(question) has state which is judged and/or is felt to not have beendone;_(— —)into_OUTPUT_ reply has state which is judged and/or is feltto have been performed;_;_,

[0583] and the general form of say( ) is given by

[0584] _ALGORITHM_ (someone) says (something) {(someone) pronounceswords;_},

[0585] “‘=’ is higher class ‘algorithm-of-process’ of ‘answer’.”

[0586] “‘act’ is higher class ‘algorithm-of-process’ of ‘answer’.”

[0587] “say’ is higher class ‘algorithm-of-process’ of ‘answer’.”

[0588] “‘pronounce’ is higher class ‘algorithm-of-process’ of ‘say’.”

[0589] As another example, let me discuss the lexical meaning of theverb, ‘ask’. It is used in sentences such as, “That kids always askingawkward questions.” In the {circle over (∘)}“Longman dictionary ofcontemporary English”, the verb ‘ask’ is defined as “to say something inorder to get an answer.”

[0590] First, I interpret this definition that, ‘state of (process) ofasking’ is divided twofold into ‘to have been performed’ and the ‘not tohave been performed’. In this sense, this ‘dichotomy of ‘quality’’ makesthe verb ‘answer’ more analytical and precise than the verb ‘say’. Inother words, the meaning of the verb ‘ask’ contains the information ofhow the ‘conversation’ occurred in a form of the ‘dichotomy of‘quality’’.

[0591] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘ask’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of function” and “sentencepattern of ‘ideal thesaurus’”, as, _ALGORITHM_ (someone) asks (someoneelse) a (question) { another one = someone ;_ ideal situation = (someoneelse) answer (question) ;_ still another one = someone ;_ to perform(something) = to say (something) (someone) acts ;_ }.

[0592] _FUNCTION_ (someone) asks (someone else) a(question);_(— —)translate_INPUT_ query has state which is judged and/oris felt to not have been done, at first;_(— —)into_OUTPUT_ query hasstate which is judged and/or is felt to have been performed, atlast;_;_(—)

[0593] “‘=’ is higher class ‘algorithm-of-process’ of ‘ask’.”

[0594] “‘act’ is higher class ‘algorithm-of-process’ of ‘ask’.”

[0595] “‘say’ is higher class ‘algorithm-of-process’ of ‘ask’.”

[0596] As another example, let me discuss the lexical meaning of theverb, ‘discuss’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘discuss’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0597] _ALGORITHM_ (someone) discusses {while( ){(someone) asks;_(someone) answers;_}}.

[0598] _FUNCTION_ (someone) discusses;_(— —)translate_INPUT_ reply andquery have states which are judged to not have beendone;_(— —)into_OUTPUT_ reply and query have state which are judged tohave been performed;_;_,

[0599] It is clear, by my definition, that,

[0600] “‘ask’ and ‘answer’ are a higher class ‘algorithm-of-process’ of‘discuss’.”

[0601] In other words,

[0602] _ALGORITHM_ (someone) asks _is_higher_class_of_ALGORITHM_(someone) discusses.

[0603] _ALGORITHM_ (someone) answers _is_higher_class_of_ALGORITHM_(someone) discusses.

[0604] As another example, let me discuss the lexical meaning of theverb, ‘think’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘think’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0605] _ALGORITHM_ (someone) thinks (procedure) {someobjects=knowledge;_ something=procedure;_ (someone) makes images of someobjects have a state which is judged and/or is felt to exist assomething in (someone)’ mind;_}.

[0606] _FUNCTION_ (someone) thinks (procedure);_(— —)translate_INPUT_knowledge which is judged and/or is felt to exist in (someone)’ mind, atfirst;_(— —)into_OUTPUT_ images of knowledge have a state which isjudged and/or is felt to exist as (procedure) in (someone)’ mind, atlast;_;_,

[0607] It is clear, by my definition, that,

[0608] “‘=’ is higher class ‘algorithm-of-process’ of ‘think’.”

[0609] “‘make’ is higher class ‘algorithm-of-process’ of ‘think’.”

[0610] In other words,

[0611] _ALGORITHM_ (someone) makes (something)_is_higher_class_of_ALGORITHM_ (someone) thinks (something).

[0612] As another example, let me discuss the lexical meaning of theverb, ‘command’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘command’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0613] _ALGORITHM_ (someone) command {some objects (someone)'s aim;_something=(someone else)'s idea to attain the (someone)'s aim;_(someone) makes ideas of some objects have a state which is judgedand/or is felt to exist as (something) with a shape made of materialsubstance;_}.

[0614] _FUNCTION_ (someone) commands;_(— —)translate_INPUT_ (someone)'saim has a state which is judged and/or is felt to exist in (someone)'smind, at first;_(— —)into_OUTPUT_ idea of (someone)'s aim have a statewhich is judged and/or is felt to exist as (someone else)'s idea toattain the (someone)'s aim with a shape of material substance, atlast;_;_,

[0615] It is clear, by my definition, that,

[0616] “‘have’ is higher class ‘algorithm-of-process’ of ‘command’.”

[0617] “‘=’ is higher class ‘algorithm-of-process’ of ‘command’.”

[0618] And

[0619] “‘make’ is higher class ‘algorithm-of-process’ of ‘command’.”

[0620] In other words,

[0621] _ALGORITHM_ (someone) makes (something)_is_higher_class_of_ALGORITHM_ (someone) command.

[0622] As another example, let me discuss the lexical meaning of theverb, ‘order’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘order’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0623] _ALGORITHM_ (someone) order {some objects=(someone)'s hope to getgoods;_ something=(someone else)'s idea to sell goods;_ (someone) makesideas of some objects have a state which is judged and/or is felt toexist as (something) with a shape made of material substance;_}.

[0624] _FUNCTION_ (someone) orders _(— —)translate_INPUT_ (someone)'shope to get goods has a state which is judged and/or is felt to exist in(someone)'s mind, at first;_(— —)into_OUTPUT_ idea of (someone)'s hopeto get goods have a state which is judged and/or is felt to exist as(someone else)'s idea to sell goods with a shape of material substance,at last;_;_,

[0625] It is clear, by my definition, that,

[0626] “‘=’ is higher class ‘algorithm-of-process’ of ‘order’.”

[0627] “‘make’ is higher class ‘algorithm-of-process’ of ‘order’.”

[0628] In other words,

[0629] _ALGORITHM_ (someone) makes (something)_is_higher_class_of_ALGORITHM_ (someone) command.

[0630] As another example, let me discuss the lexical meaning of theverb, ‘control’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘control’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0631] _ALGORITHM_ (someone) controls (something) {someobjects=(someone)'s procedure;_ something=(something)'s idea to operatethe (someone)'s procedure;_ (someone) makes idea of some objects whichis judged and/or is felt to exist as (something) with a shape ofmaterial substance;_}.

[0632] _FUNCTION_ (someone) commands;_(— —)translate_INPUT_ (someone)'sprocedure has a state which is judged and/or is felt to exist in(someone)'s mind, at first;_(— —)into_OUTPUT_ idea of (someone)'sprocedure which is judged and/or is felt to exist as (something)'s ideato operate the (someone)'s procedure with a shape of material substance,at last;_;_,

[0633] It is clear, by my definition, that,

[0634] “‘=’ is higher class ‘algorithm-of-process’ of ‘command’.”

[0635] “‘make’ is higher class ‘algorithm-of-process’ of ‘command’.”

[0636] In other words,

[0637] _ALGORITHM_ (someone) makes (something)_is_higher_class_of_ALGORITHM_ (someone) command.

[0638] As another example, let me discuss the lexical meaning of theverb, ‘operate’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘operate’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0639] _ALGORITHM_ (something) operates (procedure) {someobject=(someone)'s (procedure);_ something=(something)'s work accordingto the (someone)'s (procedure);_ (someone) makes idea of some objectswhich is judged and/or is felt to exist as (something) with a shape ofmaterial substance;_}.

[0640] _FUNCTION_ (someone) operates (procedure);_(— —)translate_INPUT_(someone)'s (procedure) has a state which is judged and/or is felt toexist in (someone)'s mind, at first;_(— —)into_OUTPUT_ idea of(someone)'s (procedure) which is judged and/or is felt to exist as(something)'s work according to the (someone)'s (procedure) with a shapeof material substance, at last;_;_,

[0641] It is clear, by my definition, that,

[0642] “‘=’ is higher class ‘algorithm-of-process’ of ‘operate’.”

[0643] “‘make’ is higher class ‘algorithm-of-process’ of ‘operate’.”

[0644] In other words,

[0645] _ALGORITHM_ (someone) makes (something)_is_higher_class_of_ALGORITHM_ (someone) operates (procedure).

[0646] As another example, let me discuss the lexical meaning of theverb, ‘judge’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘judge’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0647] _ALGORITHM_ (someone) judges {while( ){(someone) thinks;_(someone) discusses;_ if((someone) gets the answer){break;_}}

[0648] It is clear, by my definition, that,

[0649] “‘discuss’, and ‘think’ are higher class ‘algorithm-of-process’of ‘judge’.”

[0650] And

[0651] “if((someone) gets the answer){break;_}” is higher class‘algorithm-of-process’ of ‘judge’.

[0652] As another example, let me discuss the lexical meaning of theverb, ‘travel’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘travel’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of function” and “sentencepattern of ‘ideal thesaurus’”, as, _ALGORITHM_ (someone) travels {another one = someone ;_ ideal situation = (someone) gets to thedestination ;_ still another one = someone ;_ to perform (something) =to move ;_ (someone) acts ;_ }.

[0653] As another example, let me discuss the lexical meaning of theverb, ‘arrive’. It is used in the sentences such as, “Give me a call tolet me know you've arrived safely.” In the {circle over (∘)}“Longmandictionary of contemporary English”, the verb ‘arrive’ is defined as “toget to the place you are going to.”

[0654] According to my opinion, I interpret this definition that, the‘quality’, ‘your position’, is divided twofold into the ‘at station’ andthe ‘in transitional pass’. In this sense, this ‘dichotomy of ‘quality’’makes the verb ‘arrive’ more analytical and precise than the verb ‘get’.In other words, the meaning of the verb ‘arrive’, contains theinformation of how the ‘journey’ occurred in a form of the ‘dichotomy of‘quality’’.

[0655] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘arrive’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, and “sentence pattern of function” as,

[0656] _ALGORITHM_ (someone) arrives {(someone) has (position) which isjudged to be and/or felt to be in transitional pass;_ while( ){(someone)travels;_ if((someone) has (position) which is judged to be and/or feltto be at a station){break;_}}}.

[0657] _FUNCTION_ (someone) arrives;_(— —)translate_INPUT_ (someone) has(position) which is in transitional pass, at first;_(— —)into_OUTPUT_(someone) has (position) which is judged to be and/or felt to be at astation, at last;_;_.

[0658] It is clear, by my definition, that,

[0659] “‘travel’ is higher class ‘algorithm-of-process’ of ‘arrive’.”

[0660] In other words,

[0661] _ALGORITHM_ (someone) travels _is_higher_class_of_ALGORITHM_(someone) arrives.

[0662] And

[0663] “‘have’ is higher class ‘algorithm-of-process’ of ‘arrive’.”

[0664] And

[0665] “if((someone) has (position) which is judged to be and/or felt tobe at a station){break;_}” is higher class ‘algorithm-of-process’ of‘arrive’.

[0666] As another example, let me discuss the lexical meaning of theverb, ‘depart’. It is used in the sentence such as, “The train forEdinburgh will depart from platform 5.”

[0667] The verb ‘depart’ can be defined as

[0668] “to get into the way when you are starting a journey.”

[0669] According to my opinion, I interpret this definition that, thequality, ‘your position’, is divided twofold into the ‘at station’ andthe ‘in transitional pass’. In this sense, this ‘dichotomy of ‘quality’’makes the verb ‘depart’ more analytical and precise than the verb ‘get’.In other words, the meaning of the verb ‘depart’, contains theinformation of how the ‘journey’ occurred in a form of the ‘dichotomy of‘quality’’.

[0670] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘depart’ can be given by usingmy “sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0671] _ALGORITHM_ (someone) departs {(someone) has (position) which isjudged to be and/or felt to be at a station;_ while( ){(someone)travels;_ if((someone) has (position) which is judged to be and/or feltto be in transitional pass){break;_}}}.

[0672] _FUNCTION_ (someone) departs;_(— —)translate_INPUT_ (someone) has(position) which is judged to be and/or felt to be at a station, atfirst;_(— —)into_OUTPUT_ (someone) has (position) which is judged to beand/or felt to be in transitional pass, at last

[0673] It is clear, by my definition, that,

[0674] “‘travel’ is higher class ‘algorithm-of-process’ of ‘depart’.”

[0675] In other words,

[0676] _ALGORITHM_ (someone) travels _is_higher_class_of_ALGORITHM_(someone) departs.

[0677] And

[0678] “‘have’ is higher class ‘algorithm-of-process’ of ‘depart’.”

[0679] And

[0680] “if((someone) has (position) which is judged to be and/or felt tobe in transitional pass){break;_}” is higher class‘algorithm-of-process’ of ‘depart’.”

[0681] As another example, let me discuss the lexical meaning of theverb, ‘continue’. It is used in sentences such as, “The fightingcontinued for a week.” In the {circle over (∘)}“Longman dictionary ofcontemporary English”, the verb ‘continue’ is defined as

[0682] “to keep happening.”

[0683] First, I interpret this definition that, ‘state of a (process)’is divided twofold into the ‘in motion’, and the ‘out of motion’. Inthis sense, this ‘dichotomy of ‘quality’’ makes the verb ‘continue’ moreanalytical and precise than the verb ‘do’. In other words, the meaningof the verb ‘continue’, contains the information of how ‘to do a(process)’ in a form of the ‘dichotomy of ‘quality’’.

[0684] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘continue’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0685] _ALGORITHM_ (someone) continue (process) {(process) has statewhich is judged to be and/or felt to be ‘in motion’;_ while( ){(someone)dose (process);_}.

[0686] _FUNCTION_ (someone) continue (process);_(— —)translate_INPUT_(process) has state which is judged to be and/or felt to be in motion,at first;_(— —)into_OUTPUT_ (process) has state which is judged to beand/or felt to be in motion, at last;_;_,

[0687] It is clear, by my definition, that,

[0688] “‘do’ is higher class ‘algorithm-of-process’ of ‘continue’.”

[0689] And

[0690] “‘have’ is higher class ‘algorithm-of-process’ of ‘continue’.”

[0691] In other words,

[0692] _ALGORITHM_ (someone) dose (process)_is_higher_class_of_ALGORITHM_ (someone) continue (process).

[0693] As another example, let me discuss the lexical meaning of theverb, ‘break’. It is used in sentences such as, “The thieves got in bybreaking a window.”

[0694] The verb ‘break’ can be defined as

[0695] “to act so as to (something) separate.”

[0696] First, I interpret this definition that, ‘state of something’ isdivided twofold into the ‘separate’, and the ‘in unity’. In this sense,this ‘dichotomy of ‘quality’’ makes the verb ‘break’ more analytical andprecise than the verb ‘act’. In other words, the meaning of the verb‘break’, contains the information how ‘to perform’ in a form of the‘dichotomy of ‘quality’’.

[0697] ‘Algorithm-of-processes’ of the verb ‘break’ can be describedusing a quasi-C code. The lexical definition of ‘break’ is given byusing my “sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0698] _ALGORITHM_ (someone) breaks (something) {(something) has statewhich is judged to be and/or felt to be in unity, at first;_ while(){(someone) deals something;_ of(something has state which is judged tobe and/or felt to be separate){break;_}}}.

[0699] _FUNCTION_ (someone) breaks (something);_(— —)translate_INPUT_(something) has state which is judged to be and/or felt to be in unity,at first;_(— —)into_OUTPUT_ (something) has state which is judged to beand/or felt to be separate, at last;_;_,

[0700] It is clear, by my definition, that,

[0701] “‘deal’ is higher class ‘algorithm-of-process’ of ‘break’.”

[0702] In other words,

[0703] _ALGORITHM_ (someone) deals something_is_higher_class_of_ALGORITHM_ (someone) breaks (something).

[0704] And

[0705] “‘have’ is higher class ‘algorithm-of-process’ of ‘break’.”

[0706] And

[0707] “if(something has state which is judged to be and/or felt to beseparate){break;_}” is higher class ‘algorithm-of-process’ of ‘break’.

[0708] As another example, let me discuss the lexical meaning of theverb, ‘bring’. It is used in sentences such as, “Did you bring anythingto drink?”

[0709] In the {circle over (∘)}“Longman dictionary of contemporaryEnglish”, the verb ‘bring’ is defined as “to take something to the placeyou are now”.

[0710] First, I interpret this definition that, ‘position of something’is divided twofold into the ‘the place you are now’ and the ‘the placefar from you’. In this sense, this ‘dichotomy of ‘quality’’ makes theverb ‘bring’ more analytically and precise than the verb ‘take’. Inother words, the meaning of the verb ‘bring’, contains the informationof how the ‘motion of take’ occurred is described using the ‘dichotomyof ‘quality’’.

[0711] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘bring’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0712] _ALGORITHM_ (someone) brings (something) {(something) has(position) which is judged to be and/or felt to be far from you, atfirst;_ while( ){(someone) takes (something);_ if((something) has(position) which is judged to be and/or felt to be at the place(someone) is now){break;_}}}.

[0713] _FUNCTION_ (someone) brings (something);_(— —)translate_INPUT_(something) has (position) which is judged to be and/or felt to be farfrom you, at first;_(— —)into_OUTPUT_ (something) has (position) whichis judged to be and/or felt to be at the place (someone) is now, atlast;_;_,

[0714] It is clear, by my definition, that,

[0715] “‘take’ is higher class ‘algorithm-of-process’ of ‘bring’.”

[0716] In other words,

[0717] _ALGORITHM_ (someone) takes (something)_is_higher_class_of_ALGORITHM_ (someone) brings (something).

[0718] And

[0719] “‘have’ is higher class ‘algorithm-of-process’ of ‘bring’.”

[0720] And

[0721] “if((something) has (position) which is judged to be and/or feltto be at the place (someone) is now){break;_}” is higher class‘algorithm-of-process’ of ‘bring’.

[0722] As another example, let me discuss the lexical meaning of theverb, ‘build’. It is used in sentences such as, “Are they going to buildon this land?”

[0723] In the {circle over (∘)}“Longman dictionary of contemporaryEnglish”, the verb ‘build’ is defined as “to make something large.”

[0724] First, I interpret this definition that, ‘state of something’ isdivided twofold into the ‘large’ and the ‘small’. In this sense, this‘dichotomy of ‘quality’’ makes the verb ‘build’ more analytical andprecise than the verb ‘make’. In other words, the meaning of the verb‘build’, contains the information of how the ‘process of making’occurred in a form of the ‘dichotomy of ‘quality’’.

[0725] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘build’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0726] _ALGORITHM_ (someone) builds (something) {(something) has statewhich is judged to be and/or felt to be small, at first;_ while(){(someone) makes (something);_ if((something) has state which hasjudged to be and/or felt to be large){break;_}}.

[0727] _FUNCTION_ (someone) build (something);_(— —)translate_INPUT_(something) has state which is judged to be and/or felt to be small, atfirst;_(— —)into_OUTPUT_ (something) has state which is judged to beand/or felt to be large, at last;_;_,

[0728] It is clear, by my definition, that,

[0729] “‘have’ is higher class ‘algorithm-of-process’ of ‘build’.”

[0730] And

[0731] “‘make’ is higher class ‘algorithm-of-process’ of ‘build’.”

[0732] In other words,

[0733] _ALGORITHM_ (someone) make (something)_is_higher_class_of_ALGORITHM_ (someone) build (something).

[0734] And

[0735] “if((something) has state which has judged to be and/or felt tobe large){break;_}” is higher class ‘algorithm-of-process’ of ‘build’.

[0736] As another example, let me discuss the lexical meaning of theverb, ‘lift’. It is used in sentences such as, “He lifted his hands in agesture of despair.” In the {circle over (∘)}“Longman dictionary ofcontemporary English”, the verb ‘lift’ is defined as

[0737] “to move something upwards into the air.”

[0738] First, I interpret this definition that, ‘position of something’is divided twofold into the ‘in the air’ and the ‘on the ground’. Inthis sense, this ‘dichotomy of ‘quality’’ makes the verb ‘lift’ moreanalytically than the verb ‘move’. In other words, the meaning of theverb ‘lift’, contains the information of how the ‘process of moving’occurred is described using the ‘dichotomy of ‘quality’’.

[0739] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘lift’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0740] _ALGORITHM_ (someone) lifts (something) {(something) has(position) which is judged to be and/or felt to be on the ground, atfirst;_ while( ){(someone) moves (something) if((something) has(position) which is judged to be and/or felt to be in theair){break;_}}}.

[0741] _FUNCTION_ (someone) lifts (something);_(— —)translate_INPUT_(something) has (position) which is judged to be and/or felt to be onthe ground, at first _(— —)into_OUTPUT_ (something) has (position) whichis judged to be and/or felt to be in the air, at last;_,

[0742] It is clear, by my definition, that,

[0743] “‘have’ is higher class ‘algorithm-of-process’ of ‘lift’.”

[0744] And

[0745] “‘move’ is higher class ‘algorithm-of-process’ of ‘lift’.”

[0746] In other words,

[0747] _ALGORITHM_ (someone) moves (something)_is_higher_class_of_ALGORITHM_ (someone) lifts (something).

[0748] And

[0749] “if((something) has (position) which is judged to be and/or feltto be in the air){break;_}” is higher class ‘algorithm-of-process’ of‘lift’.”

[0750] As another example, let me discuss the lexical meaning of theverb, ‘take’. It is used in sentences such as, “Don't forget to takeyour bag when you go.”

[0751] In the {circle over (∘)}“Longman dictionary of contemporaryEnglish”, the verb ‘take’ is defined as “to move something from oneplace to another.”

[0752] First, I interpret this definition that, ‘(position) of(something)’ is divided twofold into the ‘one place’ and the ‘anotherplace’. In this sense, this ‘dichotomy of ‘quality’’ makes the verb‘take’ more analytical and precise than the verb ‘move’. In other words,the meaning of the verb ‘take’, contains the information of how the‘process of moving’ occurred in a form of the ‘dichotomy of ‘quality’’.

[0753] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘take’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0754] _ALGORITHM_ (someone) takes (something) {(something) has(position) which is judged to be and/or felt to be one place, at first;while( ){(someone) moves (something);_ if((something) has (position)which is judged to be and/or felt to be another place){break;_}}}.

[0755] _FUNCTION_ (someone) takes (something);_(— —)translate_INPUT_(something) has (position) which is judged to be and/or felt to be oneplace, at first;_(— —)into_OUTPUT_ (something) has (position) which isjudged to be and/or felt to be another place, at last;_;_,

[0756] It is clear, by my definition, that,

[0757] “‘move’ is higher class ‘algorithm-of-process’ of ‘take’.”

[0758] In other words,

[0759] _ALGORITHM_ (someone) moves (something)_is_higher_class_of_ALGORITHM_ (someone) takes (something).

[0760] And

[0761] “‘have’ is higher class ‘algorithm-of-process’ of ‘take’.”

[0762] And

[0763] “if((something) has (position) which is judged to be and/or feltto be another place){break;_}” is higher class ‘algorithm-of-process’ of‘take’.

[0764] As another example, let me discuss the lexical meaning of theverb, ‘carry’. It is used in sentences such as, “A porter helped mecarry my luggage.” In the {circle over (∘)}“Longman dictionary ofcontemporary English”, the verb ‘carry’ is defined as “to take somethingsomewhere in your hands.”

[0765] First, I interpret this definition that means ‘lift and take’.

[0766] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘carry’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0767] _ALGORITHM_ (someone) carries (something) {(someone) lifts(something) (someone) takes (something);_}.

[0768] _FUNCTION_(— —)(someone) carries(something);_(— —)translate_INPUT_ (something) has (position) which isjudged to be and/or felt to be on the ground, atfirst;_(— —)into_OUTPUT_ (something) has (position) which is judged tobe and/or felt to be at another place, at last;_;_(—)

[0769] It should be noted that the function defined in the last sentencein “sentence pattern of function”, ‘(someone) carries (something);_’ isregarded as a composite function made by using the output of ‘(someone)lifts (something);_’ as the input of ‘(someone) takes (something);_’.This is reasonable if we carry out an inference that, if,

[0770] “something has position which is judged to be and/or felt to bein the air”,

[0771] which is a situation after ‘(someone) lifts (something);_’,

[0772] then

[0773] “something has position which is judged to be and/or felt to bein one place”,

[0774] which is a situation before “(someone) takes (something)”.

[0775] This inference is based on the fact,

[0776] _NT_ air _is_a_kind_of_BT_ one place.

[0777] In this inference, one arrives at a sentence on the basis ofother sentence by using a ‘fact’ described in a “sentence pattern of‘ideal thesaurus’”. I call this type of inference that I introduced inthe present invention, @[algorithm of sentence based object-orientedcategorical syllogism]. (See later about the detailed definition aboutthis algorithm).

[0778] It is clear, by my definition, that,

[0779] “‘lift’ is higher class ‘algorithm-of-process’ of ‘carry’.”

[0780] In other words,

[0781] _ALGORITHM_ (someone) lifts (something)_is_higher_class_of_ALGORITHM_ (someone) carries (something).

[0782] And,

[0783] “‘take’ is higher class ‘algorithm-of-process’ of ‘carry’.”

[0784] In other words,

[0785] _ALGORITHM_ (someone) takes (something)_is_higher_class_of_ALGORITHM_ (someone) carries (something).

[0786]

Lexical Definition of ‘compound verb’

A verb which is implemented as the name of an ‘algorithm-of-process’ byusing “sentence pattern of implementation of names ofalgorithms-of-processes” which contains more than twosub-‘algorithms-of-processes’ is a ‘compound verb’.

[0787] According to this definition, ‘carry’ is a ‘compound verb’.

[0788] <Lexical Definition of ‘strategy’

Strategy is a detailed foresight. An ideal strategy tells just theprocedure to be used as the measures to deal with any situation at anytime.

[0789]

Lexical Definition of ‘procedure’

A procedure is the correct way of doing something.

[0790] As another example, let me discuss the lexical meaning of theverb, ‘follow’. It is used in sentences such as, “Japanese school boysand girls are usually disciplined by their teacher to walk forming a rowin a very good manners following their teacher when they go on a picnicto prevent traffic accidents.”

[0791] The verb ‘follow’ is defined as

[0792] “to move behind something and/or someone in the same direction.”

[0793] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘follow’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as, _ALGORITHM_ (someone) follows (something){ while( ) { the direction of motion of someone = the direction ofmotion of something ;_ (someone) moves behind (something) ;_ } }

[0794] _FUNCTION_ (someone) follows (something);_(— —)translate_INPUT_(someone) is behind (something), at first;_(— —)into_OUTPUT_ (someone)is behind (something), at last;_;_,

[0795] It is clear, by my definition, that,

[0796] “‘move’ is higher class ‘algorithm-of-process’ of ‘follow’.”

[0797] In other words,

[0798] _ALGORITHM_ (someone) moves behind (something)_is_higher_class_of_ALGORITHM_ (someone) follows (something).

[0799] And

[0800] “‘=’ is higher class ‘algorithm-of-process’ of ‘follow’.”

[0801] As another example, let me discuss the lexical meaning of theverb, ‘chase’. It is used in sentences such as, “Outside the door, kidswere yelling and chasing each other.” In the {circle over (∘)}“Longmandictionary of contemporary English”, the verb ‘chase’ is defined as “toquickly follow someone and/or something in order to catch them.”

[0802] First, I interpret this definition that, ‘state of (process) offollowing to catch’ is divided twofold into the ‘to have been performed’and the ‘not to have been performed’. In this sense, this ‘dichotomy of‘quality’’ makes the verb ‘chase’ more analytical and precise than theverb ‘follow’. In other words, the meaning of the verb ‘take’, containsthe information of how the ‘process of following’ in a form of the‘dichotomy of ‘quality’’.

[0803] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘chase’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, can be described as, _ALGORITHM_ (someone)chases (something) { another one = someone ;_ ideal situation =(someone) catches (something) ;_ still another one = someone ;_ toperform (something) = to follow (something) ;_ (someone) acts ;_ }.

[0804] _FUNCTION_ (someone) chases (something);_(— —)translate_INPUT_(process) to catch has state which has not been acted, atfirst;_(— —)into_OUTPUT_ (process) to catch has state which has beenaction, at last;_;_,

[0805] It is clear, by my definition, that,

[0806] “‘follow’ is higher class ‘algorithm-of-process’ of ‘chase’”.

[0807] In other words,

[0808] _ALGORITHM_ (someone) follows (something)_is_higher_class_of_ALGORITHM_ (someone) chases (something).

[0809] and

[0810] “‘act’ is higher class ‘algorithm-of-process’ of ‘chase’”.

[0811] In other words,

[0812] _ALGORITHM_ (someone) acts _is_higher_class_of_ALGORITHM_(someone) chases (something)

[0813] And

[0814] “‘=’ is higher class ‘algorithm-of-process’ of ‘chase’”.

[0815] By my definition,

[0816] any higher class ‘algorithm-of-process’ of ‘act’ and/or of‘follow’ is a higher class ‘algorithm-of-process’ of ‘chase’.

[0817] And

[0818] “‘catch’ is higher class ‘algorithm-of-process’ of ‘chase’”.

[0819] In other words,

[0820] _ALGORITHM_ (someone) catches (something)_is_higher_class_of_ALGORITHM_ (someone) chases (something)

[0821] ‘Chase’ is another example of a ‘compound verb’.

[0822] As another example, let me discuss the lexical meaning of theverb, ‘prevent’.

[0823] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘prevent’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as, _ALGORITHM_ (someone) prevent (something){ ideal situation = (something) can not happen ;_ to perform (something)= to perform (something) ;_ (someone) acts ;_ }.

[0824] _FUNCTION_ (someone) prevent (something);_(— —)translate_INPUT_(something) may happen;_(— —)into_OUTPUT_ (something) can nothappen.;_;_,

[0825] It is clear, by my definition, that,

[0826] “‘act’ is higher class ‘algorithm-of-process’ of ‘prevent’”.

[0827] In other words,

[0828] _ALGORITHM_ (someone) acts _is_higher_class_of_ALGORITHM_(someone) prevents (something).

[0829] And

[0830] “‘perform’ is higher class ‘algorithm-of-process’ of ‘prevent’”.

[0831] By my definition,

[0832] any higher class ‘algorithm-of-process’ of ‘act’ and/or ‘perform’

[0833] is a higher class ‘algorithm-of-process’ of ‘prevent’.

[0834] As another example, let me discuss the lexical meaning of theverb, ‘stop’. ‘Algorithm-of-processes’ of the verb can be describedusing a quasi-C code. The lexical definition of ‘stop’ is given by usingmy “sentence pattern of implementation of names ofalgorithms-of-processes”, as, _ALGORITHM_(someone) stops (something) {(something) has state which is judged to be in motion ;_(—) (someone)performs a process ;_(—) (something) has state which is judged to be outof motion ;_(—) }.

[0835] _FUNCTION_ (someone) stops (something);_(— —)translate_INPUT_(something) has state which is judged to be in motion;_(— —)into_OUTPUT_(something) has state which is judged to be out of motion.;_;_,

[0836] It is clear, by my definition, that,

[0837] “‘perform’ is higher class ‘algorithm-of-process’ of ‘stop’”.

[0838] In other words,

[0839] _ALGORITHM_ (someone) performs a (process)_is_higher_class_of_ALGORITHM_ (someone) stops (something).

[0840] And

[0841] “‘have’ is higher class ‘algorithm-of-process’ of ‘stop’”.

[0842] In other words,

[0843] _ALGORITHM_ (something) has state that is judged to be in motion_is_higher_class_of_ALGORITHM_ (someone) stops (something).

[0844] _ALGORITHM_ (something) has state that is judged to be out ofmotion _is_higher_class_of_ALGORITHM_ (someone) stops (something).

[0845] By my definition, any higher class ‘algorithm-of-process’ of‘perform’ and/or ‘have’ is a higher class ‘algorithm-of-process’ of‘stop’.

[0846] As another example, let me discuss the lexical meaning of theverb, ‘catch’. It is used in sentences such as, “If the guerrillas catchyou, they will kill you, and vv.” In the {circle over (∘)}“Longmandictionary of contemporary English”, the verb ‘catch’ is defined as “tostop someone after you have been chasing them and prevent them fromescaping.” First, I interpret this definition that means ‘stop andtrap’.

[0847] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘catch’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0848] _ALGORITHM_ (someone) catches (someone else) {(someone) stops(someone else);_ (someone) prevents (someone else) from escaping;_}.

[0849] _FUNCTION_ (someone) catches (someoneelse);_(— —)translate_INPUT_ (someone else) has state which is judged tobe and/or felt to be free, at first;_(— —)into_OUTPUT_ (someone else)has state which is judged to be and/or felt to be bound, at last;_;_,

[0850] It is clear, by my definition, that,

[0851] “‘stop’ is higher class ‘algorithm-of-process’ of ‘catch’.”

[0852] In other words,

[0853] _ALGORITHM_ (someone) stops (someone else)_is_higher_class_of_ALGORITHM_ (someone) catches (someone else).

[0854] And

[0855] “‘prevent them from escaping’ is higher class‘algorithm-of-process’ of ‘catch’.”

[0856] In other words,

[0857] _ALGORITHM_ (someone) prevents (someone else) from escaping_is_higher_class_of_ALGORITHM_ (someone) catches (someone else).

[0858] This is another example of a ‘compound verb’.

[0859] Here just I have shown how the procedure described by a sentence‘someone catches someone else’ is implemented (=how the detail proceduredescribed by a sentence ‘someone catches someone else’ is embodied). Thesentence describes an abstract and general situation. Now I will showhow the procedure described by a sentence describing more practicalsituation is implemented. Here, I give an example in which the proceduredescribed by a sentence “A cat catches a rat” is implemented:

[0860] _ALGORITHM_a cat catches a rat {someone=a cat;_ someone else=arat;_ someone stops someone else;_ someone prevents someone else fromescaping;_}.

[0861] In this sentence described in “sentence pattern of implementationof names of algorithms-of-processes”, two sentences,

[0862] someone=a cat;_,

[0863] and,

[0864] someone else=a rat;_,

[0865] where ‘=’ is the ‘assignment operator’,

[0866] are added to the sentence in “sentence pattern of implementationof names of algorithms-of-processes” implementing the procedure denotedby a sentence ‘someone catches someone else’ is implemented.

[0867] A sentence,

[0868] _ALGORITHM_ (a cat) catches (a rat) {(a cat) stops (a rat);_ (acat) prevents (a rat) from escaping;_}.

[0869] is equivalent to the sentence,

[0870] _ALGORITHM_ a cat catches a rat {someone=a cat;_ someone else=arat;_ someone stops someone else;_ someone prevents someone else fromescaping;_}.

Lexical Definition of “Instance of a Sentence Described in “SentencePattern of Implementation of Names of Algorithms-of-Processes””

[0871] If some practical and/or specific nouns are assigned to someabstract and general nouns by using the ‘assignment operator’, andsentences using such ‘assignment operator’ are added in a sentence in“sentence pattern of implementation of names ofalgorithms-of-processes”, and another sentence in “sentence pattern ofimplementation of names of algorithms-of-processes” is formed,

[0872] then,

[0873] the latter sentence in “sentence pattern of implementation ofnames of algorithms-of-processes” is an “instance of a sentence in“sentence pattern of implementation of algorithms-of-processes”” of theformer sentence in “sentence pattern of implementation of names ofalgorithms-of-processes”. If a sentence is equivalent to an “instance ofa sentence in “sentence pattern of implementation of names ofalgorithms-of-processes””, then the sentence is also regarded as an“instance of a sentence in “sentence pattern of implementation of namesof algorithms-of-processes””.

[0874] It is recommended that the practical and/or specific nouns shouldbe defined as narrower ‘descriptors’ of the abstract and general nouns,in an ‘ideal thesaurus’, by the maker of the contents of a databasedisclosed in the present invention.

[0875] For example, the sentence,

[0876] _ALGORITHM_ a cat catches a rat {someone=a cat;_ someone else arat;_ someone stops someone else;_ someone prevents someone else fromescaping;_}.

[0877] is an instance of a sentence

[0878] _ALGORITHM_ someone catches someone else {someone stops someoneelse;_ someone prevents someone else from escaping;_}.

[0879] And the sentence,

[0880] _ALGORITHM_ (a cat) catches (a rat) {(a cat) stops (a rat);_ (acat) prevents (a rat) from escaping;_}.

[0881] is also an instance of a sentence

[0882] _ALGORITHM_ someone catches someone else {someone stops someoneelse;_ someone prevents someone else from escaping;_}.

[0883] And it is recommended that the practical and/or specific noun‘cat’ and/or ‘rat’ should have be defined as a narrower ‘descriptor’ of‘someone’ and/or ‘someone else’, by the maker of a database disclosed inthe present invention when he makes an ‘ideal thesaurus’.

[0884] As another example, let me discuss the lexical meaning of theverb, ‘put’. It is used in sentences such as, “Put these carriages onthe table.”

[0885] The verb ‘put’ is defined as

[0886] “to make something still on something else.”

[0887] ‘Algorithm-of-processes’ of the verb can be described using aquasi-C code. The lexical definition of ‘put’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0888] _ALGORITHM_ (someone) puts (something) {

[0889] (something) is at somewhere;_(—)

[0890] (someone) moves (something);_(—)

[0891] (something) is standstill on (something else);_(—)

[0892] }.

[0893] _FUNCTION_ (someone) puts (something);_(— —)translate_INPUT_(something) is at somewhere;_(— —)into_OUTPUT_ (something) is standstillon (something else);_;_,

[0894] It is clear, by my definition, that,

[0895] “‘be’ is higher class ‘algorithm-of-process’ of ‘put’.”

[0896] In other words,

[0897] _ALGORITHM_ (something) is standstill on (something else)_is_higher_class_of_ALGORITHM_ (someone) puts (something).

[0898] And

[0899] “‘move’ is higher class ‘algorithm-of-process’ of ‘put’.”

[0900] In other words,

[0901] _ALGORITHM_ (someone) moves along _is_higher_class_of_ALGORITHM_(someone) puts (something).

[0902] As another example, let me discuss the lexical meaning of theverb, ‘walk’. It is used in sentences such as, “We must have walked tenmiles today.” In the {circle over (∘)}“Longman dictionary ofcontemporary English”, the verb ‘walk’ is defined as “to move alongputting one foot in front of the other.”

[0903] ‘Algorithm-of-process’ of the verb can be described using aquasi-C code. The lexical definition of ‘walk’ is given by using my“sentence pattern of implementation of names ofalgorithms-of-processes”, as,

[0904] _ALGORITHM_ (someone) walks {while({(someone) puts another footin front of the other;_ (someone) moves along;_}.}.

[0905] _FUNCTION_ (someone) walks;_(— —)translate_INPUT_ (someone) hasstate that is judged to be and/or felt to be out of motion, atfirst;_(— —)into_OUTPUT_ (someone) has state that is judged to be and/orfelt to be in motion, at last;_;_,

[0906] It is clear, by my definition, that,

[0907] “‘put a foot’ is higher class ‘algorithm-of-process’ of ‘walk’.”

[0908] In other words,

[0909] _ALGORITHM_ (someone) puts a foot _is_higher_class_of_ALGORITHM_(someone) walks.

[0910] “‘move along’ is higher class ‘algorithm-of-process’ of ‘walk’.”

[0911] In other words,

[0912] _ALGORITHM_ (someone) moves along _is_higher_class_of_ALGORITHM_(someone) walks.

[0913] A classification table of some intransitive verbs and sometransitive verbs whose lexical defined are given above is listed in FIG.23.

[0914] From my point of view, the dominant of ‘ideal verbs’ except for‘be’ and/or ‘=’ are usually a ‘black box’; Here, a ‘black box’ isideally a ‘mapping’ whose detailed way how the mapping is implemented(=embodied) is unknown; About the information of the implementation ofthe mapping of such a ‘black box’, only the abstract definitions of‘ideal verbs’ using my “sentence pattern of function”, such as shownabove, is given.

[0915] It is clear from above discussions, that an ‘ideal verb’ isdefined in two steps. In the first step, the geometric space and/oraction divided twofold according to a ‘dichotomy of ‘quality’’. In thesecond step, more abstract verb is shown as a method of to make a thingand/or action change from one of the division to the other in a finitetime step. In other words, the dominant of the verbs are a ‘black box’,which maps the state of a matter and/or situation from one of thedivision to the other in a finite time step. As will be shown later inthe present invention, I will give the lexical definition of @[algorithmof sentence based object-oriented categorical syllogism]. If and when,@[algorithm of sentence based object-oriented categorical syllogism]isused, then, the verbs can be used in propositions for reasoning ifobject-oriented knowledge base systems disclosed in the presentinvention, even while the details of the mechanism with which the verbmaps are unknown.

[0916] A ‘compound verb’ can be defined using several ‘ideal verbs’.This ‘lexical definition of an ‘ideal verb’ according to a ‘dichotomy of‘quality’’ is very useful, because the two fold division of geometricspace can be well defined if a critical boundary is definedmathematically. In an ideal cases, these are defined mathematically.

[0917] The ‘lexical definition of a compound verb using “sentencepattern of implementation of names of algorithms-of-processes” on thebasis of other compound verbs and/or ideal verbs’ is also well defined.Both of the ‘lexical definition of an ideal verb using “sentence patternof implementation of names of algorithms-of-processes” on the basis of a‘dichotomy of ‘quality’’ and using “sentence pattern of classification’”and ‘definition of a compound verb using “sentence pattern ofimplementation of names of algorithms-of-processes” is reasonablyreadable to human beings.

3.1.3.4. “Lexical Definitions of ‘Ideal Verbs’” Given in ‘IdealDictionaries’

[0918] Before giving the lexical definition of an ‘ideal dictionary’,let me show here an example of a way in which how a natural polysemousverb, ‘abuse’, is defined in an ‘ideal dictionary’. As I mentionedbefore, a natural polysemous verb, ‘abuse’ has two lexical meanings. Igive a name to each of these two lexical meanings of the naturalpolysemous noun ‘abuse’ as

[0919] ‘abuse

use

’, and, ‘abuse

say

’.

[0920] In other words, as I mentioned before, in the present example,these two ‘ideal verbs’ are used as a name denoting a lexical meaning ofthe natural polysemous verb ‘abuse’. In an ‘ideal dictionary’, first, itis recommended that these two ‘ideal verbs’, as well as, the three‘ideal nouns’, which I have mentioned before in the present invention,should be listed in a sentence described in what I call a “sentencepattern of a list of the names of the lexical meanings of a naturalword”. The lexical definition of “sentence pattern of a list of thenames of the lexical meanings of a natural word” will be given later inthe present invention. But here, I just only show, as an example, asentence described in “sentence pattern of a list of the names of thelexical meanings of a natural word” for the natural polysemous noun‘abuse’, this time in a complete form:

[0921] _ListOfLexicalMeaningsOf_ abuse _—>_(— —)

Noun KW

_=_(— —)(abuse

usage

)_,_(abuse

word

)_,_(abuse

treatment

)_._

Verb_KW

_=_(— —){abuse

use

}_,_{abuse

say

}_.

[0922] The detailed explanation about this complete form of a sentencedescribed in “sentence pattern of a list of the names of the lexicalmeanings of a natural word” for the natural polysemous word ‘abuse’ willbe shown later in the present invention. According to a lexicaldefinition given later in the present invention, I call a sentencewritten in “sentence pattern of a list of the names of the lexicalmeanings of a natural word”, a ‘means for storing the list of lexicalmeanings of a natural word’. For detail, see the lexical definition of“sentence that store the list of lexical meanings of a natural word” andthe lexical definition of ‘means for storing the list of lexicalmeanings of a natural word’, which will be given later in the presentinvention. If an ‘ideal dictionary’ that has a recommended constitutionis to be constructed, then, it is recommended that “a key describedusing ‘means for storing the list of lexical meanings of a naturalword’” should be used as a components of an ‘ideal dictionary’, in thecase when the lexical definition a verb is given in an ‘idealdictionary’ as well as in the case when the lexical definition a noun isgiven in an ‘ideal dictionary’. (See FIG. 6).

[0923] As I mentioned before in the present invention, it is recommendedthat an ‘ideal dictionary’ should have another component. That is, it isrecommended that an ‘ideal dictionary’ should comprise not only “a keydescribed using ‘means for storing the list of lexical meanings of anatural word’” but also what I call “keys giving lexical definition of alexical meaning” in a case when the lexical definition a verb is givenin an ‘ideal dictionary’. (See FIG. 6)

[0924] As I described before, A “key giving lexical definition of an‘ideal noun’” and/or a “key giving lexical definition of an ‘idealverb’” is a “key giving lexical definition of a lexical meaning”. Thislexical definition is schematically shown in FIG. 6. I have alreadygiven the lexical definition of “key giving lexical definition of an‘ideal noun’”. The lexical definition of a “key giving lexicaldefinition of an ‘ideal verb’” will be given later in the presentinvention. Hire, now, first, I will give an example in which the way thelexical definition of the ‘ideal verb’, ‘abuse’ is given.

[0925] As the lexical meaning of an ‘ideal verb’, for example, ‘abuse

use

’, in some dictionaries, sentences like

[0926] “to deliberately use something such as power of authority, forwrong purpose”,

[0927] are used as a lexical meaning of the natural verb ‘abuse’. In thepresent invention, I analyze this lexical meaning into, three sentences,

[0928] 1) ‘abuse

use

’ is classified as one style of ‘use’,

[0929] 2) during the process when someone abuses

use

power of authority, in the first step, wrong purpose is not achieved,and in the second step, someone uses power of authority, and in the laststep, wrong purpose is achieved.

[0930] “Sentence 1)” is what I call a “sentences that store data ofclassification table”, whose lexical definition will be given later inthe present invention. It is recommended in an object-oriented knowledgebase system disclosed in the present invention, that a “sentences thatstore data of classification table” should be formally described in whatI call “sentence pattern of classification”, which will be defined laterin the present invention. Hire, now, I will give an example in which theway how the “the sentence 1)” is formally described in my “sentencepattern of classification” in a simplified way, as follows:

[0931] _ALGORITHM_ use _is_higher_class_of_ALGORITHM_ abuse

use

.

[0932] How the “the sentence 1)” is formally described in my “sentencepattern of classification” in a complete way will be shown later in thepresent invention. According to my lexical definition given later in thepresent invention, a “sentences that store data of classification table”is called a ‘means for storing data of ideal classification table’. Inan object-oriented knowledge base system, however, ‘means for storingdata of ideal classification table’ is not necessarily an indispensablecomponent of an ‘ideal dictionary’, because, an ‘ideal classificationtable’ almost always contains ‘means for storing data of idealclassification table’. Therefore, in the ‘ideal thesaurus’ schematicallyshown in FIG. 6, the component, “keys described using ‘means for storingdata of ideal classification table’, is omitted.

[0933] By my definition which has already given in the presentinvention, “sentence 2)” is a sentence described in what I call“sentence pattern of implementation of names ofalgorithms-of-processes”. It is recommended that in an object-orientedknowledge base system in the present invention, a sentence described in“sentence pattern of implementation of names of algorithms-of-processes”should be described in a formal way. For example, in the case of abuse

use

, the following formal sentence is recommended to be used:

[0934] _ALGORITHM_(— —){abuse

use

}_ {wrong purpose is not achieved, at first;_ someone uses power ofauthority;_ wrong purpose is achieved, at last},

[0935] In this example,

[0936] the verb, ‘{abuse

use

}_’,

[0937] is implemented as the name of an ‘algorithms-of-processes’ byusing the body of the ‘algorithms-of-processes’, “{wrong purpose is notachieved, at first;_ someone uses power of authority;_ wrong purpose isachieved, at last}”.

[0938] This example has somewhat simplified style. An example withcomplete style will be shown later. According to my definition which hasalready been given in the present invention, a sentence described in“sentence pattern of implementation of names of algorithms-of-processes”is a ‘means for implementation of names of algorithms-of-processes’. Ingeneral, it is recommended that “keys described using ‘means forimplementation of names of algorithms-of-processes’” should be used as a“key giving lexical definition of an ‘ideal verb’” in an object-orientedknowledgebase system, disclosed in the present invention. This isschematically shown in FIG. 6.

[0939] “Sentence 2)” also shows that ‘abuse

use

’ has a function to change the present situation from, “wrong purpose isnot achieved” to the final situation “wrong purpose is achieved”.According to a lexical definition given later in the present invention,This can be described by using a sentence described in “sentence patternof function” as,

[0940] _FUNCTION_ {abuse

say

}_(— —)translate_INPUT_ wrong purpose is not achieved, atfirst;_(— —)into_OUTPUT_ wrong purpose is achieved, at last.

[0941] This sentence in “sentence pattern of function” describes how theverb _{abuse

say

}_ works as a function, by explicitly showing the input and the outputof the function. According to a lexical definition given later in thepresent invention, a sentence described in “sentence pattern offunction” is a ‘means for describing a function used as a rule’. Ingeneral, it is recommended that “keys described using ‘means fordescribing a function used as a rule’” should be used as a “key givinglexical definition of an ‘ideal verb’” in an object-orientedknowledgebase system disclosed in the present invention. This isschematically shown in FIG. 6.

[0942] Roughly speaking, I claim that a polysemous ‘natural-verb’corresponds to a concept, and each lexical meaning of the polysemous‘natural-verb’ corresponds to a category. I claim that in ideal cases,an ‘algorithm-of-process’ corresponds to ‘a category’. In ideal cases, Iregard an ‘ideal verb’ as the name of an ‘algorithm-of-process’. And Iregard a ‘name-of-classification-item’ as the ‘ideal verb’ registered inan ‘ideal classification table’.

Lexical Definition of a “Key Giving Lexical Definition of an ‘IdealVerb’”

[0943] A “key giving lexical definition of an ‘ideal verb’” is asentence which gives lexical definition of an ‘ideal verb’. It isrecommended that a “key described using ‘means for implementation ofnames of algorithms-of-processes’” and a “key described using ‘means fordescribing the function of a verb’” should be used as a “key givinglexical definition of an ‘ideal verb’” in an object-oriented knowledgebase system disclosed in the present invention.

[0944] This lexical definition is schematically shown in FIG. 6.

[0945] The lexical definition of ‘means for describing the function of averb’ will be given later in the present invention.

[0946]

Lexical Definition of an ‘ideal dictionary’ (Part 2 about lexicaldefinition of ‘ideal verbs’)

An ‘ideal dictionary’ is a kind of dictionary. When the lexical meaningof an ‘ideal verb’ is given in an ‘ideal dictionary’, then, it isrecommended that a ‘means for implementation of names ofalgorithms-of-processes’ and ‘means for describing the function of averb’ should be used.

[0947] An ‘ideal dictionary’ is usually used in an object-orientedknowledge base system disclosed in the present invention. It isrecommended, in an ‘ideal dictionary’, that the information about thelist of not only the ‘ideal nouns’ but also the ‘ideal verbs’corresponding to the lexical meanings of a natural polysemous word,should be described in a sentence in “sentence pattern of a list of thenames of the lexical meanings of a natural word”. See the lexicaldefinition of “sentence pattern of a list of the names of the lexicalmeanings of a natural word”, which has already given in the presentinvention.

[0948] It is also recommended that in an ‘ideal dictionary’, ‘thecontent’ of ‘each of the lexical meanings of ‘natural-verb’’ (i.e. ‘thecontent’ of ‘one set of the particular qualities characterizing acategory which is used to represent linguistically ‘the idea of image ofa process’’) should be explained in sentences in what I call “sentencepattern of implementation of names of algorithms-of-processes” and/or inwhat I call a sentence in “sentence pattern of function” and/or in somecases in a sentence described in what I call “sentence pattern ofclassification”. The lexical definition of “sentence pattern ofclassification” and of “sentence pattern of function” will be givenlater in the present invention.

[0949] According to my lexical definition given later in the presentinvention, a sentence described in “sentence pattern of classification”is called a ‘means for storing data of classification table in a formalway’. But ‘means for storing data of classification table in a formalway’ is not an essential part of an ‘ideal dictionary’, because the sameinformation is usually stored in an ‘ideal classification table’ (SeeFIG. 3). It should be noted that the description in an ‘idealdictionary’ and an ‘ideal classification table’ should be consistent.One of the best way of keeping such consistency is to eliminate all thesentences described in “sentence pattern of classification” from an‘ideal dictionary’, when the object-oriented knowledge base systemdisclosed in the present invention is very large. In this case, it isrecommended that only the pointer to the sentences described in“sentence pattern of classification” should be stored in the ‘idealdictionary’.

[0950] Of course, other media than characters and/or letters in a text,for example, such as pictures, photos, sounds, and/or other multimediamay be used as an accessory of an ‘ideal dictionary’.

3.1.3.5. ‘Ideal Classification Table’

[0951]

Lexical Definition of ‘ideal classification table’

‘Ideal classification table’ is a classification table in which ‘idealverb’ is classified. It is recommended, in a ‘classification table’,sentences used to classify ‘ideal verbs’ should be described in my“sentence pattern of classification”, whose definition will be givenlater in the present invention. Such a ‘classification table’, is an‘ideal classification table’. This issue is schematically shown in FIG.3

3.1.4. ‘Ideal Dictionary’

[0952] Let me show here an example; the natural word ‘abuse’ is apolysemous. If one consults the Longman “Dictionary of ContemporaryEnglish”, then, one finds that the natural word ‘abuse’ is used both asa noun and as a verb. The ‘natural-noun’ ‘abuse’ is a polysemous, andthe ‘natural-verb’‘abuse’ is also a polysemous.

[0953] According to my definition, a lexical meaning of the‘natural-noun’ ‘abuse’ corresponds to an ‘ideal noun’. For the sake ofsimplicity, let us assume here a case in which all these ‘ideal nouns’are ‘descriptors’. That is, let us assume here a case in which all these‘ideal nouns’ are registered in a ‘thesaurus’.

[0954] According to my definitions and assumptions, a lexical meaning ofthe ‘natural-verb’ ‘abuse’ corresponds to an ‘ideal verb’. For the sakeof simplicity, let us assume here a case in which all these ‘idealverbs’ are ‘name-of-classification-item’. That is, let us assume here acase in which all these ‘ideal verbs’ are registered in a‘classification table’.

[0955] At this moment, I do not explicitly shown the way in detail how averb such as ‘abuse’ is systematically classified. (See later parts ofthe present invention about details of how I classify verbs); I justcomment at this moment that verbs are classified on the basis of what Icall “sentence pattern of implementation of names ofalgorithms-of-processes”, which I disclose in the present invention.

[0956] Instead of giving strict description here, I just give here onesimple example of a hierarchy existing among verbs; A verb ‘abuse’ meansan instance of a procedure of ‘use’ under a special situation. In thissense, the verb ‘abuse’ is classified as lower class of a verb ‘use’ inthe present invention.

[0957] From now on, a ‘descriptor’ is described in parenthesis, using_(and)_. From now on, a ‘name-of-classification-item’ is described inparenthesis, using _{and }_.

[0958] Let us show some detailed example.

[0959] Lexical meanings of ‘natural-noun’ ‘abuse’ includes,

[0960] 1)“the use of something in a way that should not be used”; Let medenote this lexical meaning by a ‘descriptor’, _(abuse

usage

)_,

[0961] 2)“rude and/or offensive things that someone says to someoneelse”; Let me denote this lexical meaning by a ‘descriptor’, _(abuse

word

)_,

[0962] and,

[0963] 3) “cruel or violent treatment, often sexually, especiallysomeone that you should look after”; Let me denote this lexical meaningby a ‘descriptor’, _(abuse

treatment

)_(—)

[0964] On the other hand, lexical meanings of ‘natural-verb’ ‘abuse’includes,

[0965] 1)“to deliberately use something such as power of authority, forwrong purpose”; Let me denote this lexical meaning by a‘names-of-classification-items’, _{abuse

use

}_,

[0966] and,

[0967] 2)“to say rude and/or offensive things to someone”; Let me denotethis lexical meaning by a ‘names-of-classification-items’, _{abuse

say

}_.

[0968] Part of the information thus given can be described in a formalway, if “sentence pattern of a list of the names of the lexical meaningsof a natural word” disclosed in the present invention, is used; That is,

[0969] _ListOfLexicalMeaningsOf_ abuse _—>_(— —)

NounKW

_=_(— —)(abuse

usage

)_,_(abuse

word

)_,_(abuse

treatment

)_._

VerbKW

_=_(— —){abuse

use

}_,_{abuse

say

}_.,

[0970] in which, ‘descriptors’ associated with the natural word ‘abuse’are enumerated below ‘_—>_(— —)

Noun_KW

_=_’, and are parenthesized in _( and )_. And‘names-of-classification-items’ associated with the natural word ‘abuse’are enumerated below ‘._

VerbKW

_=_’ and are parenthesized in _{ and }_, punctuated by ‘,’.

[0971]

Lexical Definition of “sentence pattern of a list of the names of thelexical meanings of a natural word”

General form of “sentence pattern of a list of the names of the lexicalmeanings of a natural word” is given by,

[0972] _ListOfLexicalMeaningsOf_ *** _—>_(— —)

Noun KW

_=_(— ****, . . . , ****,) _****)_,_(****)_, . . . ,_(****)_._

VerbKW

_=_ *****, . . . , *****,_{*****}_,_{*****}_, . . . ,_*****}_.,

[0973] where, the ‘***’ is a natural word listed in a usual dictionary,and a ‘****’ is an ‘ideal noun’ associated with the natural word. And an‘ideal noun’ parenthesized in _( and )_ is a ‘descriptor’. And a ‘*****’is an ‘ideal verb’ associated with the natural word. And an ‘ideal verb’parenthesized in _{ and }_ is a ‘name-of-classification-item’. That is,a ‘_(****)_’ and a ‘****’ corresponds to one of the lexical meanings ofthe natural word ‘***’ used as a noun. And, a ‘_{*****}_’ and a ‘*****’corresponds to one of the lexical meanings of the natural word ‘***’used as a verb.

[0974]

Lexical Definition of “sentences that store the list of lexical meaningsof a natural word”

A sentence written in “sentence pattern of a list of the names of thelexical meanings of a natural word” and/or in its equivalent is a“sentence which store the list of lexical meanings of a natural word”.

Lexical Definition of ‘Means for Storing the List of Lexical Meanings ofa Natural Word’

[0975] “Sentence which stores the list of lexical meanings of a naturalword” and/or something that stores the information of it, is a ‘meansfor storing the list of lexical meanings of a natural word’.

[0976]

Lexical Definition of an ‘ideal dictionary’

An ‘ideal dictionary’ is a kind of dictionary disclosed in the presentinvention. It is recommended, in an ‘ideal dictionary’, that theinformation about the list of not only the ‘ideal nouns’ but also the‘ideal verbs’ corresponding to the lexical meanings of a naturalpolysemous word, should be described in a sentence in “sentence patternof a list of the names of the lexical meanings of a natural word”. It isrecommended that in an ‘ideal dictionary’, ‘the content’ of ‘each of thelexical meanings of ‘natural-verb’’ should be explained in sentences in“sentence pattern of implementation of names of algorithms-of-processes”and/or in a sentence in “sentence pattern of function” and/or when thesystem is small, in a sentence in “sentence pattern of classification”.(See later discussion about the details of “sentence pattern ofclassification” and “sentence pattern of function”). It is recommendedthat in what I call an ‘ideal dictionary’, ‘the content’ of ‘each of thelexical meanings of ‘natural-nouns’’ is explained in sentences in“sentence pattern of definition of object” and/or when the system issmall, in sentences in “sentence pattern of ‘ideal thesaurus’”. (See thelexical definition of “sentence pattern of definition of object” givenelsewhere in the present invention). (See the lexical definition of“sentence pattern of ‘ideal thesaurus’” given elsewhere in the presentinvention). Of course, other media than characters and/or letters, suchas pictures, photos, sounds, and/or multimedia may be used as anaccessory in an ‘ideal dictionary’.

[0977] First, in an ‘ideal dictionary’ of an object-oriented knowledgebase system disclosed in the present invention,

[0978] it is recommended

[0979] that the lexical definition of an ‘ideal noun’, ‘* * * *’, shouldbe given, as a lexical meaning of a ‘natural-noun’, and that the lexicaldefinition of an ‘ideal noun’, should be given in a formal way usingsentences in “sentence pattern of definition of object”,

[0980] and

[0981] that when the ‘ideal noun’ is a ‘descriptor’, the relationbetween the ‘ideal noun’ and its broader ‘descriptors’, should be given,as an additional lexical meanings of the ‘ideal noun’, in a formal wayusing sentences in “sentence pattern of ‘ideal thesaurus’”, when theobject-oriented knowledge base system is small, and else if theobject-oriented knowledge base system is large, then, the pointer to thesentences in “sentence pattern of ‘ideal thesaurus’” should be stored inan ‘ideal dictionary’, and the body of the sentences in “sentencepattern of ‘ideal thesaurus’” should not be stored in an ‘idealdictionary’

[0982] Second, in an ‘ideal dictionary’ of an object-oriented knowledgebase system disclosed in the present invention,

[0983] it is recommended

[0984] that the lexical definition of an ‘ideal verb’, ‘* * * * *’,should be given, as a lexical meanings of a ‘natural-verb’, in a formalway in sentences in “sentence pattern of implementation of names ofalgorithms-of-processes”

[0985] and

[0986] that when the ‘ideal verb’ is a ‘name-of-classification-item’,the relation between the ‘name-of-classification-item’ and its higherclass ‘names-of-classification-items’ should be given, as additionallexical meanings of the ‘ideal verb’, in a formal way using sentences in“sentence pattern of classification”, if the object-oriented knowledgebase system is small, and, else if the object-oriented knowledge basesystem is large, the pointer to the sentences in “sentence pattern ofclassification” should be stored in the ‘ideal dictionary’, and the bodyof the sentences in “sentence pattern of classification” should bestored in the ‘ideal dictionary’ should not be stored in an ‘idealdictionary’.

[0987] If I summarize above discussion, “If one wants to put verbs whoselexical meaning is defined in an ‘ideal dictionary’ in order, then, itis recommended that one should use ‘classification table’. And if onewants to put ‘ideal nouns’ whose lexical meaning is defined in an ‘idealdictionary’ in order, then it is recommended that one should use‘thesaurus’.

3.1.5. “Sentence pattern of Association”

[0988] Let us get back to the issue of descriptors in databases and inknowledge bases. As a matter of fact, for expert makers and/or expertusers of a database, it is an easy task to tell appropriate‘descriptors’ just expressing the idea, when natural words are given tothem. But as different thesauruses are used in different databases. Andas thesaurus is revised from time to time, this task is difficult evenfor experts even in the field of their own specialty.

[0989] As a way out of this problem, I disclose in the present inventiona way in which makers of contents of a database pile up data in which‘descriptors’ and/or ‘names-of-classification-items’ are correlated withcorresponding context of ‘natural-nouns’ and/or of ‘natural-verbs’. Inother words, I disclose “sentence pattern of association” here in thepresent invention with which to formalize such data. Before describingthe detail of this method strictly, I give explanation by usingexemplifying data in the form of “sentence pattern of association”, stepby step.

[0990] As a matter of fact, in the context of

[0991] “‘spoiled sheriff's abuse of power in a Western film”,

[0992] the word ‘abuse’ means (abuse

usage

)_.

[0993] On the other hand, in the context of

[0994] “stream of abuse”,

[0995] the word ‘abuse’ means (abuse

word

)_.

[0996] In most cases, as I mentioned here, the context specifies thelexical meaning of a word.

[0997] <<Lexical Definition of “sentences that store data providing theability of association”>> “Sentences that store data providing theability of association” are sentences in which a context and‘descriptors’ and/or ‘names-of-classification-items’ that are associatedwith the context comprising natural words are shown.

[0998] <<Lexical Definition of means for storing data providing theability of association>> “Sentence which stores data providing theability of association” and/or something that stores the information ofit, is a means for storing data providing the ability of association.

[0999] I regard each of the previously shown two examples of contexts,“spoiled sheriff's abuse of power in Western films” and “stream ofabuse” as an example of “the least context of natural words necessary todetermine the ‘descriptors’ and/or the ‘names-of-classification-items’corresponding uniquely to the polysemous”. It is recommended that“sentences that store data providing the ability of association” shouldbe recorded in “sentence pattern of association”. The lexical definitionof “sentence pattern of association” will be given later in the presentinvention. As an example of sentences in “sentence pattern ofassociation”, we can show the following two sentences,

[1000] _Association_spoiled sheriff's abuse of power in a Western film_->_(——)

Noun_KW

_=_(——)(spoiled sheriff

man

)_, _(abuse

usage

)_, _(power

authority

)_.

[1001] and

[1002] Association_ stream of abuse _->_(——)

Noun_KW

_=_(——)(stream

continuous series

)_, _(abuse

word

)_,

[1003] where a context of natural words is put between‘_Association_’and ‘_>_(——)

Noun_KW

_=_’. And the ‘descriptors’ a associated with the context are enumeratedbelow ‘_

Noun_KW

_=_’.

[1004] Let me assume a situation in which these two keys are registeredin an object-oriented knowledge base system disclosed in the presentinvention. And then, let us assume in addition a situation in which onlyfour ‘descriptors’, _(spoiled sheriff

man

)_, _(abuse

usage

)_, _(stream

continuous series

)_, and _(abuse

word

)_included in these two keys are obtained, when a user of the presentobject-oriented knowledge base system makes a retrieval using the word‘abuse’. If the user of the system meant ‘abuse’ in the sense of ‘use’,the principle target of the user of the system is only _(abuse

usage

)_, and all of _(spoiled sheriff

man

)_, _(stream

continuous series

)_, and _(abuse

word

)_ are noises. One of the noises, _(abuse

work

)_ is surely a useless noise. But as a matter of fact, the other noises_(spoiled sheriff

man

)_ is actually a word which some people associates when they hear theword _(abuse

usage

)_. I claim that this type of association gives in many cases valuableinformation that make the retrieval omission-free, rich and robust.

[1005] <<Lexical Definition of “sentence pattern of association” (Part1)>> One of the general form of “sentence patterns of association” (i.e.sentence patterns of category-concept correspondence) is defined asfollows.

[1006] _Association_ ‘necessary shortest context of a naturallanguage’_->_(——)

Noun_KW

_=__(a ‘descriptor’)_, _(a ‘descriptor’)_, . . . ,_(a ‘descriptor’)_._

Verb_KW

_=_(——){a ‘name-of-classification-item’}_, . . . , _{a‘name-of-classification-item’}_,

[1007] as the general form.

[1008]

[1009] Here, ‘necessary shortest context of a natural language’ meansthe shortest context of natural words that are necessary to uniquelydetermine the ‘descriptors’ associated with the polysemous and/or the‘names-of-classification-items’ associated with the polysemous.

[1010] As mentioned before, in ideal cases, a ‘descriptor’ is an ‘idealnoun’ registered in a ‘thesaurus’, and a ‘name-of-classification-item’is an ‘ideal verb’ registered in an ‘ideal classification table’.

[1011] Sentences, clauses, and/or phrases may be used as such a context.And even a single isolate word may also be used as such a ‘context’. Thecontext of natural words and the ‘descriptors’ are divided by ‘_->_’.The ‘descriptors’ associated with the ‘necessary shortest context of anatural language’ are enumerated below ‘_

Noun_KW

_=_’, and are parenthesized in ‘_(‘ and ’)_’, punctuated by ‘, ’; It isrecommended that as narrower ‘descriptors’ as possible should be putbelow ‘_

Noun_KW

_=_’. And it is recommended that when such a ‘descriptor’ is put below _

Noun_KW

_=, broader terms of the ‘descriptor’ should not be put below ‘_

Noun_KW

_=’. The present object-oriented knowledge base system knows whichdescriptor is a boarder descriptor of which descriptor. If the maker ofthe contents of the object-oriented knowledge base records such thebroader terms of the ‘descriptor’, scarcely any merit is obtained, butunnecessary redundancy, which causes serious confusion when the systemis updated, is caused. The ‘names-of-classification-items’ associatedwith ‘necessary shortest context of a natural language’ are enumeratedbelow ‘_

Verb_KW

_=_’, and are parenthesized in ‘_{‘ and ’}_’, punctuated by ‘, ’; It isrecommended that as lower ‘names-of-classification-items’ as possibleshould be put below ‘_

Verb_KW

_=_. And if one dose not want confusion during the update of thecontents of the ‘object-oriented knowledge base’ of the system, then, itis recommended that, when such a ‘name-of-classification-item’ is putbelow ‘_

Verb_KW

_=_’, higher class ‘names-of-classification-items’ of the‘name-of-classification-item’ should not be put below the ‘_

Verb_KW

_=_’.

Lexical Definition of ‘Means for Storing Data Providing the Ability ofAssociation in a Strict Way’

[1012] Sentence in “sentence pattern of association” and/or somethingthat stores the information of it, is a ‘means for storing dataproviding the ability of association in a strict way’.

[1013] ‘Means for storing data providing the ability of association in astrict way’ is a kind of ‘means for storing data providing the abilityof association’ which has a formal style. This is shown schematically inFIG. 8.

[1014] <<Lexical Definition of ‘concept’>> In the present invention, asI claimed before, I regard, in most cases, a ‘descriptor’ and/or a‘name-of-classification-item’ as ‘a name’ expressing ‘a category’. Andin ideal cases, an ‘ideal noun’ and/or an ‘ideal verb’ is ‘a name’expressing a category. On the other hand, I call, in most cases, anatural word which a man daily uses a name expressing a ‘concept’.

[1015] A word that a man daily uses (i.e. one concept) usually belongsto more than two categories (i.e. has more than two lexical meanings).In other words, a natural word is usually polysemous. Therefore, in ausual dictionary, in most cases, more than two lexical meanings aregiven for a natural word. As I mentioned before, ‘a category’corresponds one to one to one of the lexical meanings of a natural wordin an ‘ideal dictionary’. In other words, an ‘ideal dictionary’ can bedefined as a tool to find ‘categories’ (i.e. to consult the lexicalmeanings) which is associated to a concept (i.e. to a natural word).

[1016] It is recommended that an ‘ideal dictionary’ disclosed in thepresent invention should contain a sentence in “sentence pattern of alist of the names of the lexical meanings of a natural word” as animportant parts of the information about a natural word. That is, it isrecommended that all the ‘ideal nouns’ and/or all the ‘ideal verbs’ usedas the name of a lexical meaning of the natural word should be listedfor a natural word in an ‘ideal dictionary’.

[1017] It is recommended that a lexical meaning of an ‘ideal noun’should be explained in sentences described in “sentence pattern ofdefinition of object” in an ideal dictionary. And if and when the ‘idealnoun’ is a ‘descriptor’, then, it is recommended that sentences in“sentence pattern of ‘ideal thesaurus’” should also be used to explainthe meaning. The body of sentences described in “sentence pattern of‘ideal thesaurus’” may be recorded in an ideal dictionary if and whenthe object-oriented knowledge base system is small. But if and when theobject-oriented knowledge base system is large, then, it is recommendedthat pointers to the sentences described in “sentence pattern of ‘idealthesaurus’”, instead of the body, should be recorded in an ‘idealdictionary’. And it is recommended that the lexical meaning of an ‘idealverb’ should be explained in sentences in “sentence pattern ofimplementation of names of algorithms-of-processes”. And if and when the‘ideal verb’ is a ‘names-of-classification-items’, then, it isrecommended that sentences described in “sentence pattern ofclassification” should also be used to explain the meaning. The body ofsentences described in “sentence pattern of classification” may bestored in an 7ideal dictionary’. But if and when the object-orientedknowledge base system is large, then, it is recommended that pointers tothe sentences described in “sentence pattern of classification”, insteadof the body, should be recorded in an ‘ideal dictionary’.

[1018] In an ideal case, a ‘thesaurus’ disclosed in the presentinvention is a system of classified ‘ideal nouns’ that has ahierarchical structure. On the other hand, in an ideal case, a‘classification table’ disclosed in the present invention is a system ofclassified ‘ideal verbs’ that has a hierarchical structure. Ahierarchical structure in an ‘ideal thesaurus’ and a hierarchicalstructure in an ‘ideal classification table’ are strictly distinguishedin the present invention. The detailed explanation of hierarchicalstructure of ‘ideal nouns’ disclosed in the present invention and thedetailed explanation of hierarchical structure of ‘ideal verbs’ will begiven later in the present invention.

3.1.6. Details of @[Algorithm of Making a List of ‘Descriptors’ Rankedin Order of Hit Frequency] and @[Algorithm of Making a List of‘Names-of-Classification-Items’ Ranked in Order of Hit Frequency]

[1019] When an object-oriented knowledge base system disclosed in thepresent invention is used, it is recommended that the user of the systemshould first search appropriate ‘descriptors’ and/or‘names-of-classification-items’ to be used in the retrieval to becarried out in the inference. It is often difficult, however, to find a“proper ‘descriptors’ to be used in a query” representing most properlythe idea which the retriever has as a form of a natural word.

[1020] In many cases, when a user wants to make such a search, a userusually makes a retrieval using a query that contains natural wordsrepresenting the user's idea. And the user tries to pick up usable‘descriptors’ and/or ‘names-of-classification-items’ from the keys thatare retrieved. In many cases, however, the same ‘descriptor’ is ‘found’repeatedly in a series of searches for ‘descriptors’ associated with anatural word; that is the case in which a same ‘descriptor’ is containedin more than two keys retrieved in one search, on the basis of thenatural word. In cases when too many keys are retrieved during thesearch of ‘descriptors’ and/or ‘names-of-classification-items’, the userof the system must pick up appropriate ‘descriptors’ out of these manykeys by eliminating ‘noise’ ‘descriptors’. This work, however, islaborsome for a usual man.

[1021] This difficulty can be avoided if a tool that picks up and sortsin the order of hit frequency, the ‘descriptors’ that are retrievedafter a query. I call the algorithm used in this tool @[algorithm ofmaking a list of ‘descriptors’ ranked in order of hit frequency]. Ifthis tool is used, the user of the system can view the list of“‘descriptors’ that are ranked in the order of hit frequency” that areassociated with the natural words the retriever inputted into acomputer. In a word, @[algorithm of making a list of ‘descriptors’ranked in order of hit frequency] makes a ordered list of the all‘descriptors’ that are contained in a key retrieved during the search.

[1022] In many cases, the rank in the list is expected to be nearly inthe order of their importance as a useful information, because in manycases, more important ‘descriptors’ are expected to be retrieved withhigher frequency. What I say here is that ‘descriptors’ stronglyassociated with the natural word is expected, with high probability, tobe expressing just exactly the user's idea. Of course, @[algorithm ofmaking a list of ‘descriptors’ ranked in order of hit frequency] can beused to list up and rank the descriptors associated with not only with anatural word but also associated with a ‘descriptor’.

[1023] If @[algorithm of making a list of ‘descriptors’ ranked in orderof hit frequency] is used, computers can be used as a tool of artificialintelligence to help human's ability of association; it works as a toolthat helps a man to carry out association with higher performance withhigher precision, within broader range on the basis of data held andshared by many people systematically and permanently.

[1024] <<Lexical Definition of @[algorithm of making a list of‘descriptors’ ranked in order of hit frequency]>> In the presentinvention, such an algorithm (an algorithm in which ‘descriptors’ whichappear in the keys retrieved during a search and/or in some cases in thekeys which share records with the hit keys are displayed in the order ofhit frequency) is an

[1025] @[algorithm of making a list of ‘descriptors’ ranked in order ofhit frequency]. Here the word ‘retrieve’ includes the retrieval using‘descriptors’, the retrieval using ‘names-of-classification-items’, andthe retrieval using natural words such as ‘next-best-natural-nouns’and/or ‘next-best-natural-verbs’. The lexical definition of‘next-best-natural-nouns’ and ‘next-best-natural-verbs’ will be givensoon in the present invention.

[1026] When @[algorithm of making a list of ‘descriptors’ ranked inorder of hit frequency] is used, user of the system has only to selectappropriate ‘descriptors’ out of the listed ‘descriptors’, and as aresult, even non experts, who do not remember ‘descriptors’ correctly,easily use the system.

Lexical Definition of “Means for Making a List of ‘Descriptors’ Rankedin Order of Hit Frequency”

[1027] @[Algorithm of making a list of ‘descriptors’ ranked in order ofhit frequency] and/or something that stores the information of it, is a“means for making a list of ‘descriptors’ ranked in order of hitfrequency”.

[1028] <<Lexical Definition of ‘next-best-natural-noun’>> A noun whichis useful to be used as a key word in the present query, but is not an‘descriptor’ is a ‘next-best-natural-noun’. According to my definitions,an ‘ideal noun’ which is not listed in a ‘thesaurus’ is regarded as a‘next-best-natural-noun’.

[1029] <<Lexical Definition of ‘next-best-natural-verb’>> A verb whichis useful to be used as a key word in the present query, but is not a‘names-of-classification-items’ is a ‘next-best-natural-verb’.

[1030] According to my definition, an ‘ideal verb’ which is not listedin a ‘classification table’, as well as a natural verb, is regarded as a‘next-best-natural-verb’.

[1031] A schematic quasi-C code of an example of embodiment of@[algorithm of making a list of ‘descriptors’ ranked in order of hitfrequency] is given in Formula. 7.

[1032] It should be noted that some of the useful subroutines to processcharacters are provided in {circle over (∘)} “C-Arugorizumu Zen-Ka”.

Lexical Definition of @[Algorithm of Making a List of‘Names-of-Classification-Items’ Ranked in Order of Hit Frequency]

[1033] In a similar way, in the present invention, An algorithm in which‘names-of-classification-items’ which appear in the keys retrievedduring a search and/or in some cases in the keys which share recordswith the hit keys are displayed in the order of hit frequency, is an

[1034] @[algorithm of making a list of ‘names-of-classification-items’ranked in order of hit frequency].

[1035] A schematic quasi-C code of an example of embodiment of@[algorithm of making a list of ‘names-of-classification-items’ rankedin order of hit frequency] is given in Formula. 8A and Formula. 8B.

[1036] When @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] isused, user of the system has only to select appropriate‘names-of-classification-items’ out of the listed‘names-of-classification-items’, and as a result, even non experts, whodo not remember ‘names-of-classification-items’ correctly, easily usethe system.

Lexical Definition of “Means for Making a List of‘Names-of-Classification-Items’ Ranked in Order of Hit Frequency”

[1037] @[Algorithm of making a list of ‘names-of-classification-items’ranked in order of hit frequency] and/or something that stores theinformation of it, is a “means for making a list of‘names-of-classification-items’ ranked in order of hit frequency”.

[1038] @[Algorithm of making a list of ‘names-of-classification-items’ranked in order of hit frequency] is used as a tool to help the abilityof a user of an object-oriented knowledge base system disclosed in thepresent invention to associate ‘names-of-classification-items’ withnatural words and/or with other ‘names-of-classification-items’.

[1039] When only ‘descriptors’ and/or ‘names-of-classification-items’with high hit frequencies are used in a query, then, high performanceretrieval omitting noises can be carried out. On the other hand, wheneven ‘descriptors’ and/or ‘names-of-classification-items’ with low hitfrequencies are also used in a retrieval, then, a complete an exhaustiveretrieval with little omission can be carried out. Thus the problem ofnoise can be flexibly coped with if @[algorithm of making a list of‘descriptors’ ranked in order of hit frequency] and/or @[algorithm ofmaking a list of ‘names-of-classification-items’ ranked in order of hitfrequency] is used.

[1040] It is recommended that the “sentences that store data providingthe ability of association” described in “sentence pattern ofassociation” which have minimal context to specify ‘descriptors’ and/orto specify ‘names-of-classification-items’, should be used as the basis,on which @[algorithm of making a list of ‘descriptors’ ranked in orderof hit frequency] and/or @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] is tobe carried out, in order to make the noise minimal.

[1041] Of course, sentences in keys other than the “sentences that storedata providing the ability of association” may be used as the basis ofdata on which @[algorithm of making a list of ‘descriptors’ ranked inorder of hit frequency] and/or @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] iscarried out. Strictly speaking, a “sentence that stores data providingthe ability of association” means a sentence that stores data mainlyused to providing the ability of association. Of course, “sentences thatstore data providing the ability of association” may be used in recordsof the data as well as may be used in keys of data.

3.2. ‘Object-Oriented Natural Language Processing’

[1042] Thus far, I have disclosed a set of ideas comprising the ideathat “a noun is a name of an object”, and the idea that that sentencesin “sentence pattern of definition of object” can be used to give alexical meaning of a noun. I call natural language processing on thebasis of such ideas, an ‘object-oriented natural language processing’. Iwill disclose some methods and concepts in ‘object-oriented naturallanguage processing’ in the present invention.

3.2.1. ‘Object-Oriented Universal Grammar’

[1043] <<Lexical Definition of ‘object-oriented universal grammar’>> Asthe first discussion about ‘object-oriented natural languageprocessing’, I will disclose step by step important ideas aboutobject-oriented grammar which is universally applied to naturallanguages. I call such grammar an ‘object-oriented universal grammar’.

3.2.1.1. Details of My Interpretation of the Meaning of ‘Object’

[1044] Before discussing practical problems, I will formulate in anoriginal way, the relation between what I call ‘descriptors’ and what Icall ‘objects’ on the basis of a software engineering. In the case of‘object-oriented programming’, an object is a set of subroutines (i.e.functions) and of variables. However, most of the functions in an objectis not permitted to be used by other objects; That is, most of thefunctions in an object are concealed from other objects. Only a littlenumbers of functions in an object is ‘visible’ form other objects. Thesefunctions are often called a ‘member function’ of the object. An objectinteracts with other objects and/or with itself via its ‘memberfunction’. That is, a ‘member function’ is used as an ‘interface’.

[1045] <<Lexical Definition of ‘object’>> What whose state isrecognizable and in many cases, acts and/or responds and changes and/orkeeps the state of its own and/or state of other ‘objects’, is an‘object’.

[1046] <<Lexical Definition of ‘individual function’>> Such actionsand/or responses and/or reaction of an ‘object’, is an ‘individualfunction’ of the ‘object’. An ‘individual function’ is a new ideaoriginally disclosed in the present invention.

[1047] <<Lexical Definition of ‘individual variable’>> An ‘individualvariable’ is a parallel concept of the ‘individual function’ and is anew idea originally disclosed in the present invention. If an object ‘Y’is a part of object ‘X’, then, I regard ‘Y’ as an ‘individual variable’of ‘X’, if ‘Y’ is visible from other objects. And, if ‘Z’ is a ‘quality’which characterizes ‘X’, then, I regard ‘Z’ as an ‘individual variable’of ‘X’, if ‘Z’ is visible from other objects. If and when the ‘value’ ofan ‘individual variable’ is given, then, I judge that the state of the‘object’ is described.

[1048] For example, I regard an ‘elephant’ as an ‘object’. And I regarda ‘nose’ also as an object. A‘nose’ is a part of an ‘elephant’. And‘nose’ is visible for a man. Therefore, by my definition, I regard the‘nose’ as an ‘individual variable’ of the ‘elephant’.

[1049] A famous sentence,

[1050] “An elephant has a long nose”,

[1051] means that ‘nose’ is ‘long’. As I regard ‘nose’ as an ‘individualvariable’ of an ‘elephant’, and I regard an ‘individual variable’ a kindof a variable, it is reasonable that I regard ‘long’ as the ‘value’ ofthe ‘individual variable’.

[1052] For another example, let me show a sentence,

[1053] “The position of the sun in the sky was high at that time.”

[1054] I regard ‘the sun’ as an object. I regard ‘position’ of the sunas a visible ‘quantity’ of the ‘sun’, and, therefore, I regard‘position’ as an ‘individual variable’ of the ‘sun’. And I regard ‘high’as the ‘value’ of the ‘individual variable’.

[1055] As are shown in these two examples, adjectives can be used as the‘value’ of an ‘individual variable’.

[1056] <<Lexical Definition of ‘quality’>> Something such as size,color, feeling, and/or weight that makes one thing different from otherthing is an ‘quality’.

[1057] For example, I regard position and/or time as an ‘quality’ ofsomething in the present invention.

[1058] For another example, let me show a sentence,

[1059] “The weight of an elephant was 1500 Kg.”

[1060] I regard the ‘elephant’ an ‘object’, and I regard ‘weight’ as an‘quality’ of the ‘object’. Therefore, ‘weight’ is an ‘individualvariable’ of the ‘elephant’. The ‘1500 Kg’ is a quantity. I regard ‘1500Kg’ as the ‘value’ of the ‘individual variable’. The ‘1500’ is a number,and ‘Kg’ shows what is used as the unit of the measurement of the‘weight’.

[1061] <<Lexical Definition of ‘quantity’>> A set composed of a ‘numbermeasured from an origin’ and a ‘name of something used as the unit ofthe measurement’, is a ‘quantity’. In many cases, the ‘origin’ isexplicitly described as a part of ‘quantity’.

[1062] For example, ‘five feet from the ground’ is a ‘quantity’,wherein, ‘five’ is a number, ‘the ground’ is the origin from which the‘quality’ is measured, and ‘foot’ is something to determine the unit oflength.

[1063] As mentioned before, ‘weight’ is a ‘quality’. If the ‘quantity’of this ‘quality’ is known, then, it is possible to judge, in ascientific way, whether the ‘quality’ is heavy or light using a‘critical value’ is determined. For example, if a proposition,

[1064] “The critical value of weight with which an elephant is judged tobe heavy or light is 1000 Kg”,

[1065] is accepted, then an elephant who weights 1500 Kg, is judged tobe a heavy elephant, because 1500 Kg is heavier than the critical value,1000 Kg. Of course, fuzzy theory may be used in such a judgment.

[1066] Of course, whether an elephant is heavy or light, can be judgedby a man using his feelings and/or his emotions. As a matter of fact,most of people uses the words ‘heavy’ and/or ‘light’ even when they donot make a scientific measurement using weighting scales.

[1067] <<Lexical Definition of ‘adjective’>> A word and/or word phrasewhich describes the result of outcome of a judgment of the ‘quality’ ofan object and/or the outcome of a feeling to the ‘quality’ of theobject, is an ‘adjective’.

[1068] If and when an ‘object’ is defined lexically in an ‘idealdictionary’, then, it is recommended that lexical meaning of the‘object’ should be described using sentences in “sentence pattern ofdefinition of object”, and/or in some cases, in “sentence pattern of‘ideal thesaurus’”, etc. In the sentence in “sentence pattern ofdefinition of object”, ‘individual variables’ of the ‘object’ to bedefined and ‘individual functions’ of the ‘object’ to be defined arelisted.

[1069] In many cases, such an ‘individual variable’ itself is also an‘object’. Such an ‘individual variable’ is a variable which characterizethe state of the ‘object’ to be defined.

[1070] An ‘individual function’ is a function with which values of‘individual variables’ of its own ‘object’ and/or of other ‘objects’ arechanged and/or are kept as they are. And lexical meaning of ‘individualfunctions’ of the identified ‘object’ can be described using sentencesin “sentence pattern of implementation of names ofalgorithms-of-processes”, and/or in “sentence pattern ofclassification”, etc.

Example of ‘Objects’

[1071] Generally speaking, a cat has fur. A cat catches a rat. A catlicks its own fur. Here, I regard a ‘cat’ an ‘object’. I regard ‘fur’ an‘individual variable’ of the ‘cat’. And I regard ‘catch’ and ‘lick’ asan ‘individual functions’ of the ‘cat’. Thus, I give here a set ofterminologies as follows;

[1072] ‘Object A′≡cat’,

[1073] ‘individual variable A1′≡‘fur’,

[1074] ‘individual function A1′≡‘catch’,

[1075] ‘individual function A2′≡‘lick’,

[1076] and,

[1077] ‘individual function A3′≡‘run’.

[1078] On the other hand, generally speaking, a rat has a life. A ratruns.

[1079] Here, I regard a ‘rat’ an ‘object’. I regard ‘life’ an‘individual variable’ of the ‘rat’. I regard ‘run’ an ‘individualfunction’ of the ‘rat’. Thus, I give here a set of terminologies asfollows;

[1080] ‘Object B′≡‘rat’,

[1081] ‘individual variable B1′≡‘life’,

[1082] and

[1083] ‘individual function B1′≡‘run’.

[1084] I regard ‘alive’ and/or ‘dead’ as a value of the ‘individualvariable B1′ (i.e. ‘life’ of a ‘rat’). Thus, the action of a cat,‘catch’ (i.e. ‘individual function A1′) changes the values of‘individual variable B1′ from ‘alive’ to ‘dead’. This is an example inwhich an ‘individual function’ of an object changes (i.e. maps) thevalue of an ‘individual variable’ of other object.

[1085] (Rest of this Example of ‘objects’ will be shown soon later inthe present invention.)

3.2.1.2. ‘Ideal Transitive Verb’ and ‘Ideal Intransitive Verb’ LexicalDefinition of ‘Ideal Transitive Verb’ and ‘Ideal Intransitive Verb’

[1086] I classify ‘individual functions’ into three types;

[1087] The first type is a group of ‘individual functions’ during whosemapping, values of ‘individual variables’ of other ‘objects’ are changedand/or are kept as they are.

[1088] The second type is a group of ‘individual functions’ during whosemapping, values of ‘individual variables’ of their own ‘object’ (except‘individual variables’ describing the place and time at which there own‘object’ exists) are changed and/or are kept as they are.

[1089] And the third type is a group of ‘individual functions’ duringwhose mapping the value of ‘individual variables’ of their own ‘object’describing the place and time at which their own object exists arechanged and/or are kept as they are.

[1090] I call either of the first type of ‘individual functions’ and/orthe second type of ‘individual functions’ an ‘ideal transitive verb’, byanalogy with English grammar, when the ‘individual functions’ are an‘ideal verb’.

[1091] I call the third type of ‘individual functions’ an ‘idealintransitive verb’ by analogy with English grammar when the ‘individualfunctions’ are an ‘ideal verb’.

[1092] We have already given a lexical definition of the term ‘idealverb’ thus far in the present invention. I regard a verb as the name ofa function of an object, i.e. as the name of an ‘individual function’.

Example of ‘Objects’

[1093] (Continued)

[1094] The ‘individual function A1′ of the ‘cat’, i.e. ‘catch’,

[1095] maps

[1096] the ‘individual variable B1′, i.e. ‘life’, of ‘rat’,

[1097] from its value ‘alive’ to its value ‘dead’.

[1098] Therefore, according to my definition given just above, the‘individual function A1′ of a ‘cat’ (=one ‘object’) is judged to be an‘ideal transitive verb’ because it changes the value of ‘individualvariable B1′ of ‘rat’ (=other ‘object’).

[1099] The ‘individual function A2′ of ‘cat’, i.e. ‘lick’,

[1100] maps

[1101] the ‘individual variable A1′, (i.e. ‘fur’ of ‘cat’),

[1102] from its value ‘dirty’ to its value ‘clean’.

[1103] According to my definition, ‘individual function A2′ of ‘cat’ isjudged to be an ‘ideal transitive verb’ because ‘individual function A2′changes the value of its own ‘individual variable A1’, while ‘individualvariable A1′ describing neither the place nor time at which the ‘cat’exists.

[1104] (Rest of this Example of ‘objects’ will be shown soon later inthe present invention.)

[1105] <<Lexical Definition of ‘space variables’ and ‘time variables’>>An ‘individual variable’ characterizing the information of space atwhich its ‘object’ exists, is a ‘space variable’. An ‘individualvariable’ characterizing the information of time at which its ‘object’exists is a ‘time variables’.

[1106] According this terminology, ‘ideal intransitive verb’ is an‘individual function’ which maps only ‘space variables’ of its own‘object’ and/or only ‘time variables’ of its own ‘object’. It should benoted that ‘individual function’ which maps ‘space variables’ of other‘objects’ and/or ‘time variables’ of other ‘objects’ are called ‘idealtransitive verb’.

Example of ‘Objects’

[1107] (Continued)

[1108] For example, the ‘individual function A3′ of ‘cat’ (i.e. ‘run’)maps only its own ‘space variable’ and its own ‘time variables’, fromtheir values where the ‘cat’ is now, to the values when and where the‘cat’ will be: While, non of other values of ‘individual variables’ ofthe ‘cat’ such as ‘fur’ are changed. According to my definition, ‘run’is an ‘ideal intransitive verb’.

[1109] (End of Example of ‘objects’)

[1110] In an ideal case, for example, one can use the Cartesiancoordinate representing the center of mass of the ‘rat’, as a ‘spacevariable’ of a ‘rat’. Such a ‘space variable’ can be used when a manwants to describe the translational motion of the ‘rat’.

[1111] As another example, when one regard a ‘weathercock’ an ‘object’,one can use the ‘direction’ of the head of the ‘weathercock’ as a ‘spacevariable’ of the ‘weathercock’. The ‘value’ of ‘direction’ is either‘north’, ‘west’ ‘south’ ‘east’. Such a ‘space variable’ can be used whena man wants to describe the rotational motion of an ‘weathercock’.

[1112] I have shown that in ideal cases, an ‘ideal verb’ defined byanalogy with English grammar is used as an ‘individual function’ of an‘object’. Remember that an ‘ideal verb’ is the name of an‘algorithm-of-process’. Such an ‘algorithm-of-process’ can be regardedas an implementation (embodiment of the detail procedure) of the‘individual function’ of an ‘object’. The ‘individual function’ maps‘individual variables’ and changes their value and/or keeps as they are.

3.2.1.3. ‘Subject-Word (S)’ of an ‘Ideal verb’ Lexical Definition of‘Subject-Word (S)’ of an ‘Ideal Verb’

[1113] In the present invention,

[1114] if,

[1115] an ‘object’ has the main-information about the‘algorithm-of-process’ denoted by an ‘ideal verb’, (especially in thecases when an ‘object’ has sufficiently-rich-information with which aprogrammer can code the main-routine of the quasi-C code describing the‘algorithm-of-process’ meant by the ‘ideal verb’,)

[1116] then,

[1117] I call the name of the ‘object’ the ‘subject-word (S)’ of the‘ideal verb’.

3.2.1.4. ‘Object-Oriented-Lexical-Definition of a Noun’ by Using“Sentence Pattern of Definition of Object”

[1118] In a famous sentence,

[1119] “An elephant has a long nose”,

[1120] the ‘elephant’ is an ‘object’, ‘nose’ is ‘individual variable’ ofthe ‘object’, ‘long’ is the value of the ‘individual variable’, and ‘hasa long nose’ is predicate. It should be noted that an adjective is usedas the value of a quality.

[1121] I claim that, a sentence that has a predicate corresponds to,lines of a quasi-C code with which a C programmer defines andinitializes what C programmers call a ‘structure’.

[1122] For example, let me show here lines of a quasi-C code, whichcorresponds to the sentence ‘An elephant has a long nose’: /* Define a‘structure’ */ Struct elephant{ char nose ;_(—) } /* Initialize the‘structure’ */ elephant.nose = long ;_(—)

[1123] <<Lexical Definition of “sentences that store data that defineobjects”>> A “sentence that stores data that defines an object” is asentence in which ‘individual functions’ and ‘individual variables’possessed by an ‘object’ and/or values of such ‘individual variables’are explicitly shown.

Lexical Definition of ‘Means for Storing Data that Define Objects’

[1124] “Sentence which stores data that define objects” and/or somethingthat stores the information of it, is a ‘means for storing data thatdefine objects’.

Lexical Definition of “Sentence Pattern of Definition of Object”

[1125] One of the “sentence patterns of definition of object” has thefollowing data structure:

[1126] _OBJECT_ ***** have_VARIABLES *** which_is **,

[1127] where, ‘*****’ is the name of an ‘object’, ‘***’ is the name ofan ‘individual variable’, and ‘**’ is the ‘value’ of the ‘individualvariable’. The value of the ‘individual variable’ may be omitted in thisdata structure.

[1128] As an example, I show

[1129] _OBJECT_ elephant have_VARIABLES nose which_is long.

[1130] Another “sentence pattern of definition of object” has thefollowing data structure:

[1131] _OBJECT_ ***** _have_FUNCTION_****,

[1132] where, ***** is the name of an ‘object’, and ‘****’ is the‘individual function’.

[1133] As an example, I show

[1134] _OBJECT_ cat _have_FUNCTION_ catch a mouse

[1135] It should be noted that “sentence pattern of definition ofobject” is based on simple English grammar.

Lexical Definition of ‘Means for Storing Data that Strictly DefineObjects’

[1136] A sentence in “sentence pattern of definition of object” and/orsomething that stores the information of it, is a ‘means for storingdata that strictly define objects’.

[1137] It is recommended that sentences in “sentence pattern ofdefinition of object”,

[1138] _OBJECT_ ***** have_VARIABLES *** which_is **

[1139] or

[1140] _OBJECT_ ***** _have_FUNCTION_ ****

[1141] should be used when data that define objects is described in akey of a data of an object-oriented knowledge base system disclosed inthe present invention.

Lexical Definition of ‘Object-Oriented-Lexical-Definition of a Noun’

[1142] A sentence in “sentence pattern of definition of object” is an‘object-oriented-lexical-definition of a noun’.

3.2.1.5. Mathematical Foundations for Definition of ‘Thesaurus’ LexicalDefinition of @[Algorithm of Giving Definition of Broader ‘Object’ andNarrower ‘Object’]

[1143] Here, I formulate mathematically and recursively the idea on thebasis of which ‘narrower descriptor’ and ‘broader descriptor’ used in an‘ideal thesaurus’ is defined. Let us call the set of the ‘individualvariables’ of object X, VX. Let us call the set of the ‘individualfunctions’ of an object X, FX. Let us call the set of the ‘individualvariables’ of object Y, VY. If and when an ‘individual variable’ ofobject Y is also an object, any ‘individual variable’ of it should beregarded to be included in VY. More generally speaking, any ‘individualvariable’ of an ‘individual variable’ should be regarded as an‘individual variable’. Let us call the set of the ‘individual functions’of another object Y, FY. And any function, which is used to implementthem, should be regarded to be included in FY.

[1144] Under this definition, if

FX⊂FY,

[1145] and

VX⊂VY,   (Equation. 1)

[1146] are satisfied, then, I regard that “Object X is broader object ofobject Y, and object Y is narrower object of object X”.

[1147] The procedure described above to define broader ‘object’ andnarrower ‘object’ is an @[algorithm of giving definition of broader‘object’ and narrower ‘object’]

Lexical Definition of @[Algorithm of Giving Definition of Broader‘Descriptor’ and Narrower ‘Descriptor’]

[1148] If and when an object is judged to be a broader ‘object’ andother ‘object’ is judged to be a narrower ‘object’ by using @[algorithmof giving definition of broader ‘object’ and narrower ‘object’], then, a‘descriptor’ which is the name of the broader ‘object’ is judged to be a‘broader descriptor’, and a ‘descriptor’ which is the name of thenarrower ‘object’ is judged to be a narrower descriptor.

[1149] The procedure described above to define broader ‘descriptor’ andnarrower ‘descriptor’ is an @[algorithm of giving definition of broader‘descriptor’ and narrower ‘descriptor’]. In Formula. 10, quasi-C codewhich gives practical examples of embodiment of @[algorithm of givingdefinition of broader ‘descriptor’ and narrower ‘descriptor’] are shown.

[1150] It should be noted that when the lexical definition of@[algorithm of giving definition of broader ‘object’ and narrower‘object’] was given, it was of basic importance to know what ‘object’has what ‘individual functions’ and/or has what ‘individual variables’.It is recommended that this basically important information is stored asa data described as a sentence described in “sentence pattern ofdefinition of object”. As described before in the present invention, Icall such a sentence, a ‘means for storing data that define objects’.This point is schematically described in FIG. 6.

Lexical Definition of ‘Means for Giving Definition of Broader Descriptorand Narrower Descriptor’

[1151] @[Algorithm of giving definition of broader ‘descriptor’ andnarrower ‘descriptor’] and/or something that stores the information ofit, is a ‘means for giving definition of broader descriptor and narrowerdescriptor’.

Lexical Definition of Tool to Construct a Hierarchical System of Nounsin an ‘Ideal Thesaurus’

[1152] @[Algorithm of giving definition of broader ‘object’ and narrower‘object’] is a tool to construct a hierarchical system of nouns in an‘ideal thesaurus’.

[1153] In the present invention, I claimed that, a ‘descriptor’ is thename of an ‘object’. It should be noted, for example, that the ideal andabstract ‘object’ expressed by a word ‘any boy’ is the broadest ‘object’of all the ‘objects’ that are expressed by using a noun ‘boy’. Forexample, ‘any boy’ is a broader object of ‘Tommy’. And ‘any boy’ is abroader object of ‘Jimmy’. This is because ‘any boy’ is an object thathas minimal ‘individual variables’ and minimal ‘individual functions’that are shared by all the objects expressed by using a noun ‘boy’.

[1154] It should be noted that in the same reason, ‘any dog’ is thebroadest object of all the objects expressed by using a noun ‘dog’. Forexample, ‘any dog’ is a broader object of ‘Fido’. And ‘any dog’ is abroader object of ‘Sent Bernard dog’.

[1155] In a word, I claim that “‘any’+‘a noun’” expresses the name ofthe broadest object of all the objects denoted by using the ‘noun’.

[1156] In addition, it should be noted that a proposition,

[1157] A sparrow flies in the air”

[1158] can be analyzed by using a sentence described in “sentencepattern of implementation of names of algorithms-of-processes” as, “asparrow flies in the air” { any bird = a sparrow ;_(—) any bird flies inthe air ;_(—) },

[1159] where ‘=’ is an assignment operator. This means, according to mydefinition which will given later in the present invention, that “Anybird flies in the air” is the name of a “higher class‘algorithm-of-process’” of “A sparrow flies in the air”. This means thatif ‘a sparrow’ is assigned to ‘any bird’ in any proposition that isinferred from the proposition “Any bird flies in the air”, then, theproposition thus obtained by the assignment, is always a propositionthat can be inferred from the sentence “A sparrow flies in the sky”. Ina word, the way in which the outcome of a general and abstract inferenceis applied to a special case, can be explicitly descried if we use asentence described in “sentence pattern of implementation of names ofalgorithms-of-processes”. This logic is a kind of what I call@[algorithm of sentence based object-oriented categorical syllogism],the lexical definition of which will be given later in the presentinvention.

[1160] In other words, I claim that the definition of broader‘descriptor’ and narrower ‘descriptor’ in an ‘ideal thesaurus’ isidentical with definition of broader object and narrower object based onequation 1. When I use the terms ‘broader term’ and ‘narrower term’ inthe present invention, the definition of ‘broader’ and ‘narrower’ isbased of equation 1.

Lexical Definition of “Data of ‘Thesaurus’”

[1161] Thus,

[1162] “data of ‘ideal thesaurus’” described is

[1163] data that explicitly describe the relation of broader term andnarrower term between ‘descriptors’ in the sense of above definition.

3.2.1.6. “Sentence Pattern of ‘Ideal Thesaurus’” Lexical Definition of“Sentence Pattern of ‘Ideal Thesaurus’”

[1164] The “sentence pattern of ‘ideal thesaurus’” has a data structureas

[1165] _NT_ ***** _is_a_kind_of_BT_ ***.

[1166] where ‘*****’ is a narrower ‘descriptor’, and ‘***’ is a broader‘descriptor’. A‘thesaurus’ can be described by sentences used as keysdescribed in the “sentence pattern of ‘ideal thesaurus’”. By definition,a sentence in “sentence pattern of ‘ideal thesaurus’” is a “sentencethat store data of thesaurus”.

Lexical Definition of ‘Means for Storing Data of Ideal Thesaurus in aFormal Way’

[1167] Sentence in “sentence pattern of ‘ideal thesaurus’” and/orsomething that stores the information of it, is a ‘means for storingdata of ideal thesaurus in a formal way’. It is recommended that a“sentence that stores data of ‘ideal thesaurus’” should be described in“sentence pattern of ‘ideal thesaurus’”. This issue is schematicallyshown in FIG. 2.

[1168] As mentioned before, the lexical meaning of a ‘descriptor’ isusually explained in a sentence described in “sentence pattern ofdefinition of object” and/or in a sentence described in “sentencepattern of‘ideal thesaurus’”. Let us show here an example using the caseof _(abuse

usage

)_:

[1169] _NT_(— —)(abuse

usage

)_(— —)is_a_kind_of₁₃ BT_(— —)(use

usage

)_,

[1170] and

[1171] _OBJECT_(— —)(abuse

usage

)_ have_VARIABLES purpose which_is judged and/or is felt to be viciouson a basis of moral ethics.

[1172] Let us give another example using the case of _(abuse

word

)_:

[1173] _NT_(— —)(abuse

word

)_(— —)is₁₃ a_kind_of_BT_(— —)(word

talk

)_,

[1174] and

[1175] _OBJECT_(— —)(abuse

word

)_ have_VARIABLES ethical nature which_is judged and/or is felt to bevicious,

[1176] and

[1177] _OBJECT_(— —)(abuse

word

)_ have_FUNCTION_ to upset people.

[1178] I do not mean that only these sentence patterns of mine should beused to explain a lexical meaning of ‘descriptors’ in theobject-oriented knowledge base system disclosed in the presentinvention. For example, multi media including pictures, photographs,sounds, and animations of course may be used to explain a lexicalmeaning of ‘descriptors’ in the object-oriented knowledge base systemdisclosed in the present invention.. What I claim is that formalexplanation of lexical meaning of ‘descriptors’ using my sentencepatterns is readily used for the purpose of formal inference usingcomputers. The algorithms for such inference will be given later in thepresent invention.

[1179] Sentences described in “sentence pattern of ‘ideal thesaurus’”are a kind of knowledge that is classified as ‘facts’. Sentences usingmy sentence pattern are highly readable to human, and have high power ofexpression.

3.2.1.7. Mathematical Foundations for Definition of ‘ClassificationTable’

[1180] A sentence in “sentence pattern of implementation of names ofalgorithms-of-processes” is used as a “sentence that stores data onwhich classification is carried out”.

Lexical Definition of @[Algorithm of Giving Definition of Higher Class‘Algorithm-of-Process’ and Lower Class ‘Algorithm-of-Process’]

[1181] Let me assume that

[1182] px1, px2, . . . , and, pxN, are an sub-‘algorithm-of-processes’,and the implementation of an ‘algorithm of a process’, PX, is given by asentence in “sentence pattern of implementation of names ofalgorithms-of-processes”, as

[1183] _ALGORITHM_PX {px1;_px2;_* * * ;_pxN;_}.

[1184] And let me assume in addition that the implementation of analgorithm of another process, PY, is given by a sentence in “sentencepattern of implementation of names of algorithms-of-processes”, as

[1185] _ALGORITHM_PY {py1;_py2;_* * * pyM;_}.

[1186] where, of course, ‘if( ){ }’ clauses, ‘for( ){ }’, and/or ‘while(){ }’ clauses in terms of C language may be inserted between ‘{‘ and ’}’as a ‘pxn;_’, and/or as a ‘pyn;_’. Nesting is of course OK.

[1187] Under these assumptions, if

[1188] {px1;_px2;_* * * ;_pxN;_}

[1189] is part of

[1190] {py1;_py2;_* * * ;_pyM;_},

[1191] then, I say that

[1192] “PX is higher class ‘algorithm-of-process’ of PY”,

[1193] and

[1194] “PY is lower class ‘algorithm-of-process’ of PX”.

[1195] An algorithm with which to judge that an ‘algorithm-of-process’is higher class ‘algorithm-of-process’ and that other‘algorithm-of-process’ is lower class ‘algorithm-of-process’ is an@[algorithm of giving definition of higher class ‘algorithm-of-process’and lower class ‘algorithm-of-process’].

[1196] In Formula. 12, quasi-C code which gives practical examples ofembodiment of @[algorithm of giving definition of higher class‘algorithm-of-process’ and lower class ‘algorithm-of-process’] areshown.

Lexical Definition of ‘Means for Giving Definition of Higher ClassAlgorithm-of-Process and Lower Class Algorithm-of-Process’

[1197] @[Algorithm of giving definition of higher class‘algorithm-of-process’ and lower class ‘algorithm-of-process’] and/orsomething that stores the information of it, is a ‘means for givingdefinition of higher class algorithm-of-process and lower classalgorithm-of-process’.

[1198] It should be noted that when the lexical definition of@[algorithm of giving definition of higher class ‘algorithm-of-process’and lower class ‘algorithm-of-process’] was given, it was of basicimportance to know what ‘algorithm-of-process’ has whatsub-‘algorithm-of-process’. This basically important information isstored as a sentence described in “sentence pattern of implementation ofnames of algorithms-of-processes”. As I defined before in the presentinvention, I call such a sentence ‘means for implementation of names ofalgorithms-of-processes’. This point is schematically described in FIG.6.

Lexical Definition of Tool to Construct a Hierarchical System of Verbsin an ‘Ideal Classification Table’

[1199] @[Algorithm of giving definition of higher class‘algorithm-of-process’ and lower class ‘algorithm-of-process’] is a toolto construct a hierarchical system of verbs in an ‘ideal classificationtable’.

[1200] By definition,

[1201] if an ‘algorithm-of-process’, PZ, is implemented by an “instanceof a sentence in sentence pattern of implementation of names ofalgorithms-of-processes””, and an ‘algorithm-of-process’, PU, isimplemented by a sentence in “sentence pattern of implementation ofnames of algorithms-of-processes” which is used as the basis on whichthe “instance of a sentence in “sentence pattern of implementation ofnames of algorithms-of-processes”” is composed,

[1202] then,

[1203] PU is a higher class ‘algorithm-of-process’ of PZ,

[1204] because

[1205] if a sentence which assigns a specific noun to a general nounusing an ‘assignment operator’, ‘=’, is added to the sentence describedin “sentence pattern of implementation of names ofalgorithms-of-processes” implementing PU,

[1206] then,

[1207] “instance of a sentence in “sentence pattern of implementation ofnames of algorithms-of-processes”” implementing PZ is obtained.

[1208] For example, an ‘algorithm-of-process’ whose name is “any birdflies in the air”, is implemented by a sentence described in “sentencepattern of implementation of names of algorithms-of-processes”, as, “anybird flies in the air” { any bird flies in the air ;_(—) }.

[1209] This sentence is trivial, but I regard this sentence as asentence described in “sentence pattern of implementation of names ofalgorithms-of-processes””.

[1210] On the other hand, ‘algorithm-of-process’, whose name is “asparrow flies in the air” is implemented by a sentence described by an“instance of a sentence in “sentence pattern of implementation of namesof algorithms-of-processes””. “a sparrow flies in the air” { any bird =a sparrow ;_(—) any bird flies in the air ;_(—) }.

[1211] In this example, the ‘algorithm-of-process’ whose name is “anybird flies in the air”, is a higher class ‘algorithm-of-process’ of the‘algorithm-of-process’ whose name is “a sparrow flies in the air”,because the ‘algorithm-of-process’, “{ any bird flies in the air ;_(—)}”, is included in the ‘algorithm-of-process’, “{ any bird = a sparrow;_(—) any bird flies in the air ;_(—) }”.

[1212] In the present invention, I claimed that it is recommended thatan ‘ideal verb’ should be used as a ‘names-of-classification-items’corresponds uniquely to a name of an ‘algorithm-of-process’. In otherwords, a ‘names-of-classification-items’ is a name of an‘algorithm-of-process’ which is listed in a ‘classification table’ of anobject-oriented knowledge base system disclosed in the presentinvention.

[1213] <<Lexical Definition of higher class‘name-of-classification-item’ and lower class‘name-of-classification-item’>> When a pair of higher class‘algorithm-of-process’ and lower class ‘algorithm-of-process’ areassigned by using @[algorithm of giving definition of higher class‘algorithm-of-process’ and lower class ‘algorithm-of-process’], then,the name-of-classification-item used as the name of the higher class‘algorithm-of-process’ is a higher class ‘name-of-classification-item’,and the name of the lower class ‘algorithm-of-process’ is a lower class‘name-of-classification-item’.

[1214] In other words, the definition of higher class and lower class inan ideal ‘classification table’ is given on the basis of themathematical definition of higher and lower class ‘algorithm-of-process’given just above.

[1215] Many existing practical classification system can be explainedusing these definitions. An example is the logical classification systemof biology. Almost all life has DNA. According to an up to date biology({circle over (∘)} “out of control” p.415), DNA contains the rule ofgrowth, and controls the process of growth of embryo. As a result,almost all living thing corresponds one to one to ‘algorithm-of-processof growth stored in DNA, and can be logically classified within ourscope of classification.

[1216] Thus, “sentences which store data on which classification iscarried out” ideally means a sentence in “sentence pattern ofimplementation of names of algorithms-of-processes”. And a sentence in“sentence pattern of classification” is based on the way ofclassification described above.

Lexical Definition of “Sentences that Store Data of ClassificationTable”

[1217] A “sentences that store data of classification table” is asentence used to describe an ‘ideal thesaurus’. Strictly speaking,

[1218] either a sentence whose form is,

[1219] “‘***’ is higher class of ‘****’”,

[1220] where ‘***’ and ‘****’ are ‘names-of-classification-items’,

[1221] and/or a sentence whose form is equivalent to “‘***’ is higherclass of ‘***’”,

[1222] is a “sentences that store data of classification table”.

Lexical Definition of Means for Storing Data of Classification Table

[1223] “Sentence which stores data of classification table” and/orsomething that stores the information of it, is a means for storing dataof classification table. It is recommended that a “sentence pattern ofclassification” should be described in “sentence that stores data ofclassification table”. This situation is schematically shown in FIG. 3.

[1224] <<Lexical Definition of “sentence pattern of classification”>> Itis recommended that “sentence which stores data of classificationtable?’ should be described in what I call “sentence pattern ofclassification”. The “sentence pattern of classification” has thefollowing data structure:

[1225] _ALGORITHM_ **** _is_higher_class_of_ALGORITHM_ ***.

[1226] where ‘***’ is the name of higher class ‘algorithm-of-process’,and ‘****’ is the name of lower class ‘algorithm-of-process’. Thedefinition of the words, ‘higher class’ and ‘lower class’ used here hasbeen given in the present invention. In the present invention, a verb isregarded as a name of an ‘algorithm-of-process’. Names of various‘algorithms-of-processes’ are classified in a ‘classification table’disclosed in the present invention. A ‘name-of-classification-item’ in a‘classification table’ disclosed in the present invention is usually aname of an ‘algorithm-of-process’. Usually, a‘name-of-classification-item’ is an ‘ideal verb’. By definition, asentence in “sentence pattern of classification” is a “sentence thatrecord data of classification table”.

Lexical Definition of ‘Means for Storing Data of Classification Table ina Formal Way’

[1227] A sentence in “sentence pattern of classification” and/orsomething that stores the information of it, is a ‘means for storingdata of classification table in a formal way’.

[1228] Specifically when keys described in “sentence pattern ofclassification” are used to construct a ‘classification table’, ‘***’ isthe name of the higher class ‘names-of-classification-items’, and ‘****’is the name of lower class ‘names-of-classification-items’.

[1229] The issue described here above is shown schematically in FIG. 3

[1230] As I mentioned before, in an object-oriented knowledge basesystem disclosed in the present invention, the lexical meaning of a‘name-of-classification-item’ is usually explained in a sentence in“sentence pattern of implementation of names of algorithms-of-processes”and/or in a sentence in “sentence pattern of classification”. Let meshow here an example using the case in which the lexical definition of a‘name-of-classification-item’, _{abuse

use

}_, is given:

[1231] _ALGORITHM_(— —){abuse

use

}_ {wrong purpose is not achieved at first;_use authority;_wrong purposeis achieved at last;_},

[1232] and

[1233]_ALGORITHM_(— —){use}_(— —)is_higher_(—class)_of_ALGORITHM_(— —){abuse

use

}.

[1234] Let me show here another example using the case of _abuse

say

}_:

[1235] _ALGORITHM_(— —)abuse

say

}_ {wrong purpose is not achieved at first;_ say;_ wrong purpose isachieved at last;_},

[1236] and

[1237]_ALGORITHM_(— —){say}_(— —)is_higher_class_of_ALGORITHM_(— —){abuse

say

}_.

[1238] I do not mean that only these sentence patterns of mine should beused to explain a lexical meaning of a ‘name-of-classification-item’ inthe object-oriented knowledge base system disclosed in the presentinvention. For example, multi media including pictures, photographs,sounds, and animations of course may be used. What I claim is thatformal explanation of lexical meaning of ‘name-of-classification-item’using my sentence patterns is readily used for the purpose of formalinference using computers. The algorithms for such inference will begiven later. Such sentences using my sentence patterns are a kind ofknowledge that is classified into ‘facts’. Sentences using my sentencepattern are highly readable to human, and have high power of expression.

[1239] Remember that the lexical meanings of a natural word can belisted in a sentence in “sentence pattern of a list of the names of thelexical meanings of a natural word”. As mentioned before, in ideal casesin which the name of a lexical meaning is an ‘ideal verb’ used as a‘name-of-classification-item’, the lexical meaning of the ‘ideal verb’can be usually explained in a sentence in “sentence pattern ofimplementation of names of algorithms-of-processes” and/or in a sentencein “sentence pattern of classification”.

[1240] In another case, as mentioned before, in ideal cases in which thename of a lexical meaning is an ‘ideal noun’ used as a ‘descriptor’, thelexical meaning of a ‘ideal noun’ is usually explained in a sentence in“sentence pattern of definition of object” and/or in a sentence in“sentence pattern of ‘ideal thesaurus’”.

[1241] Thus, it should be noted that a rich power of expressionapproximation the richness of information of existing dictionarieswritten in a natural language is obtained if sentences in these sentencepatterns of mine are used.

[1242] Here I make an important comment about “sentence pattern ofimplementation of names of algorithms-of-processes”: Remember the caseof implementation of a function used in a source code written in Clanguage. In this case, essentially, all the details of the algorithm ofa C function must be explicitly described as the implementation. Ofcourse, the set of subroutines called API (application programminginterfaces provided by ©Microsoft) are black box to any Windowsprogrammer outside ©Microsoft. However, complete data about completeimplementation of any API for Windows programming should exist insomewhere in ©Microsoft. In this sense, the implementation of anyfunction in any source code of Windows program written by anyone usingAPI is complete. That is, in this sense, no black box remains in anysource code of Windows program written by any programmer using API.

[1243] If a complete black box were to remain in a source code of aprogramming language, it can not be translated into machine language,and can not be executed by any computer.

[1244] In a sharp contrast to this situation of source code written inC, black boxes may remain in a sentence described in “sentence patternof implementation of names of algorithms-of-processes”. This is because,as will be explained later, the inference mechanism of anobject-oriented knowledge base system disclosed in the present inventioncan carry out ‘inference’ based on sentences including complete blackboxes.

[1245] I claim that, essentially, most of the ‘natural-verbs’ used innatural languages are black boxes. In this sense, the power ofexpression of an object-oriented knowledge base system disclosed in thepresent invention approximates to the power of expression of naturallanguages. The details of the algorithms used in the inference mechanismof an object-oriented knowledge base system disclosed in the presentinvention will be disclosed later in the present invention.

[1246] In the present invention, I have claimed ‘that a verb is the nameof an ‘algorithm-of-process’. It should be noted that such a concept hasnot conventionally been proposed.

3.3. An Object-Oriented Knowledge Base System 3.3.0. An RecommendedStyle of an Object-Oriented Knowledge Base System Disclosed in thePresent Invention (Overview)

[1247] An object-oriented knowledge base system disclosed in the presentinvention is a kind of ‘theorem proving’ system. In other words, forexample, the system can give an answer to a question asked by users ofthe system. Varieties of industrial applicability of an object-orientedknowledge base system disclosed in the present invention will be shownlater in the present invention. FIG. 1 shows a recommended constitutionof such an object-oriented knowledge base system disclosed in thepresent invention. Wherein, the body of the information of theobject-oriented knowledge base system is stored on a ‘means for storingknowledge base system’. I have already given the lexical definition of‘means for storing knowledge base system’, in the present invention.However, according to the lexical definition, for example, a ‘hard disk’of a personal computer is a kind of ‘means for storing knowledge basesystem’. And a removable disk such as a floppy disk is also a kind of‘means for storing knowledge base system’ (See FIG. 21).

[1248] A ‘digital computing system’ (for example, a computer) is theplatform on which the object-oriented knowledge base system performs aninference, for example to answer the question. Users of theobject-oriented knowledge base system operates the ‘digital computingsystem’, via an ‘input device’ (for example, via a ‘key board’ of thecomputer) and/or via an ‘output device’ (for example, via a ‘display’ ofthe computer). This situation is schematically shown in FIG. 1 . Said‘means for storing knowledge base system’ may be either a part of the‘digital computing system’ and/or not a part of the ‘digital computingsystem’ (see FIG. 19 and FIG. 20). What is important is that in mostcases, a ‘digital computing system’ dose a job according to theprocedure that is recorded as a body of information of anobject-oriented knowledge base system disclosed in the present inventionon a ‘digital computing system’. Lexical definition of ‘input device’and/or ‘output device’ will be given later in the present invention.

[1249] Not only users of the system, but also, plurality of other‘digital computing systems’ connected to the original ‘digital computingsystem’ via ‘input device’ and/or via ‘output device’ (for example, viaa ‘Network Interface Card’), may operate the ‘digital computing system’(See FIG. 1), for example to make use of the object oriented knowledgebase system working on the ‘digital computing system’ and/or to be madeuse of by the object oriented knowledge base system working on the‘digital computing system’.

[1250] Such a case occurs, for example, in the following situation; Asdescribed later in the present invention, an object-oriented knowledgebase system disclosed in the present invention solves a problem step bystep to give the answer to the question on a computer. In many cases, ifand when one problem is tried to be solved in a step of reasoning, then,the problem is broken down into more simple problems, and as the result,usually more than two sub problems that are to be tried to be solved inthe next step are obtained, at the end of the present step. If and whenmore than tow other computers are connected to the computer, via theinternet and/or via a local area network, then, the computer maydispatch a plurality of sub problems to the other computers. Afterwards,the other computers should return the answers, afterwards.

[1251] An object-oriented knowledge base system disclosed in the presentinvention which has a recommended style (See FIG. 1 ) comprises:

[1252] 1) an ‘object-oriented knowledge base’, whose lexical definitionwill be given just below, in the present invention.

[1253] 2) an ‘object-oriented knowledge base management system’, whoselexical definition will also be given just below in the presentinvention, and

[1254] 3) a ‘means for carrying out an inference’, whose detailedexplanation will be given later in the present invention.

[1255] A ‘means for carrying out an inference’ is a kind of “‘mechanismfor inference’ of a ‘knowledge base’”, and an object-oriented knowledgebase system disclosed in the present invention proves a theorem by usinga ‘means for carrying out an inference’. The style of the reasoning ofthe ‘means for carrying out an inference’ is an opportunistic reasoning.That is, inference of ‘means for carrying out an inference’ is carriedout step by step, and an each step of opportunistic reasoning is eithera forward reasoning and/or a backward reasoning. This is schematicallyshown in FIG. 9. When, thus, ‘means for carrying out an inference’ worksto prove a theorem, the ‘means for carrying out an inference’ uses an‘object-oriented knowledge base’ as a basis of knowledge on which atheorem is to be proved. One knowledge and/or more than two knowledge inan ‘object-oriented knowledge base’ are used in each step of theopportunistic reasoning. In an object-oriented knowledge base disclosedin the present invention, the theorem to be proved, which I call‘hypothetical proposition that is the target of the present step ofopportunistic reasoning’, is broken down into plurality of hypotheticalpropositions. The aim of the each step of the opportunistic reasoning isto prove a ‘hypothetical proposition that is the target of the presentstep of opportunistic reasoning’. This is schematically shown in FIG.10. The lexical definition of ‘hypothetical proposition that is thetarget of the present step of opportunistic reasoning’ will be givenlater in the present invention. A recommended constitution of each stepof opportunistic reasoning is schematically shown in FIG. 11. The detailof step by step opportunistic reasoning will be described later in thepresent invention in “§3.3.11.2.3. Step by Step Opportunisticreasoning”. The lexical definition of an ‘object-oriented knowledgebase’ will be given just below in the present invention. The detail of‘means for carrying out an inference’ will be explained later in thepresent invention mainly by using Formula. 2 (See “§3.3.11. Algorithm ofAssociation and Reasoning (i.e. algorithm of ‘Inference mechanism’) ofan Object-oriented knowledge base system disclosed in the Presentinvention”).

[1256] An ‘object-oriented knowledge base management system’ is a toolwhich is used when a maker of an object-oriented knowledge base systemdisclosed in the present invention manages an ‘object-oriented knowledgebase’. To say more precisely, for example, if and when a maker of anobject-oriented knowledge base system disclosed in the presentinvention, wants to expand the ‘object-oriented knowledge base’ and/orwants to make a renewal of the ‘object-oriented knowledge base’, then,it is recommended that the maker of the object-oriented knowledge basesystem disclosed in the present invention should use ‘object-orientedknowledge base management system’. An ‘object-oriented knowledge basemanagement system’ comprises an ‘ideal dictionary’, ‘means for givingdefinition of broader descriptor and narrower descriptor’, and ‘meansfor giving definition of higher class algorithm-of-process and lowerclass algorithm-of-process’ (See FIG. 1 and FIG. 6).

[1257] For example, if and when a maker of an object-oriented knowledgebase system disclosed in the present invention expands and/or makes arenewal of an ‘ideal thesaurus’, which is a part of an ‘object-orientedknowledge base’ (see FIG. 1 ), according to ‘means for giving definitionof broader descriptor and narrower descriptor’ wherein the maker uses an‘ideal dictionary’ as the basis of data on which ‘means for givingdefinition of broader descriptor and narrower descriptor’ is carriedout, then, the maker of the object-oriented knowledge base systemdisclosed in the present invention can expand and/or make a renewal ofan ‘ideal thesaurus’ systematically in a consist way. The lexicaldefinition of ‘object-oriented knowledge base management system’ will begiven later in the present invention, and a recommended constitution ofan ‘object-oriented knowledge base management system’ will be shownschematically by using FIG. 6.

[1258] And for another example, if and when a maker of anobject-oriented knowledge base system disclosed in the present inventionexpands and/or makes a renewal of an ‘ideal thesaurus’, which is a partof an ‘object-oriented knowledge base’ (see FIG. 1), according to ‘meansfor giving definition of higher class algorithm-of-process and lowerclass algorithm-of-process’ wherein the maker uses an ‘ideal dictionary’as the basis of data on which ‘means for giving definition of broaderdescriptor and narrower descriptor’ is carried out, then, the maker ofthe object-oriented knowledge base system can expand and/or make arenewal of an ‘ideal classification table’ systematically in a consistway.

[1259] When ‘means for carrying out an inference’ is carried out,‘rules’, which is a part of an ‘object-oriented knowledge base’ (seeFIG. 1), are used as the basis of knowledge on which an inference iscarried out. The lexical definition of ‘rules’ will be given just laterin the present invention. As for the contents of the ‘rules’, see FIG. 5and the explanation thereof. An almost indispensable procedure whichshould be carried out almost whenever a ‘means for carrying out aninference’ is put into practice, is what I call ‘means for carrying outsentence based object-oriented categorical syllogism’ (see FIG. 1 ).This ‘means for carrying out sentence based object-oriented categoricalsyllogism’ is a means for carrying out a special kind of syllogism,which I originally introduced in the present invention. ‘Means forcarrying out sentence based object-oriented categorical syllogism’ is aspecial method in which an ‘ideal thesaurus’ and an ‘idealclassification table’ is made full use of. And if and when ‘means forcarrying out sentence based object-oriented categorical syllogism’ isused as a tool of ‘means for carrying out an inference’, then, the rangeand/or scope in which a ‘rule’ is applicable is expanded in anexhaustive and flexible manner. And as the result, it becomes possiblethat not only mathematical formula but also Sentences described in anatural language (for example, in English) is processed in anobject-oriented knowledge base system disclosed in the presentinvention. Thus, what I call sentence based object-oriented categoricalsyllogism, which is used when the inference is carried out, makes itpossible that the object-oriented knowledge base system can process notonly mathematical equations but also linguistic sentences based on asimple English grammar.

[1260] The lexical definition of ‘means for carrying out sentence basedobject-oriented categorical syllogism’ will be given later in thepresent invention.

Lexical Definition of an ‘Object-Oriented Knowledge Base’

[1261] An ‘object-oriented knowledge base’ is a set of a plurality ofknowledge which are recorded in an object-oriented style and are used asthe basis on which inference is carried out in an object-orientedknowledge base system. It is recommended that an object-orientedknowledge base disclosed in the present invention should comprise

[1262] 1) ‘rules’,

[1263] 2) “keys described using ‘means for storing data providing theability of association’”,

[1264] 3) facts.

[1265] This recommended constitution of an object-oriented knowledgebase disclosed in the present invention is schematically shown in FIG.1.

[1266] The first component of an ‘object-oriented knowledge base’ is‘rules’.

Lexical Definition of a ‘Rule’

[1267] ‘A rule’ used in an ‘object-oriented knowledge base’, means asentence stored in the ‘object-oriented knowledge base’ which isdescribed in a form of a hypothetical proposition. And a rule is used asa knowledge used in a step of reasoning during an inference carried outby using an object-oriented knowledge base system.

[1268] According to the lexical definition of what I call ‘means forstoring data used as rules’, which will be given later in the presentinvention, it is reasonable in most cases that ‘a rule’ used in an‘object-oriented knowledge base’ should be described as a “key describedusing ‘means for storing data used as rules’” (see FIG. 5).

[1269] If and when an object-oriented knowledge base system disclosed inthe present invention is to be used to carry out an inference especiallyin a branch of mathematics and/or in a branch of physics, then, it isrecommended that a ‘rule’ in the ‘object-oriented knowledge base’ shouldbe described using what I call “sentence pattern of physical and/ormathematical rules”. “Sentence pattern of physical and/or mathematicalrules”]has a style based on English grammar, and a sentence written in“sentence pattern of physical and/or mathematical rules” is extremelyreadable for a man who can speak English. The lexical definition of“sentence pattern of physical and/or mathematical rules” will be givenlater in the present invention. As will be described later in thepresent invention, I call a sentence described in “sentence pattern ofphysical and/or mathematical rules”, a ‘means for storing data used asrules n a formal way’. (See the lexical definition of ‘means for storingdata used as rules in a formal way’ given later in the presentinvention). According to this naming, the same sentence I have statedjust before is paraphrased as follows; If and when an object-orientedknowledge base system disclosed in the present invention is to be usedto carry out an inference especially in a branch of mathematics and/orin a branch of physics, then, it is recommended that a “key describedusing ‘means for storing data used as rules in a formal way’” should beused as a “key described using ‘means for storing data used as rules’”(See FIG. 5).

[1270] As will be shown later in the section of“§IndustrialApplicability” of the present invention, an object-oriented knowledgebase system disclosed in the present invention can to be used in variousbranches of software engineering. In many computer-programminglanguages, such as C, Lisp, and/or APL, a subroutine in a source code ofa computer program is called a function. If and when an object-orientedknowledge base system disclosed in the present invention is to be usedto carry out an inference especially to compose a code in such ‘functionprogramming languages’, then, it is recommended that a ‘rule’ in the‘object-oriented knowledge base’ should be described using what I call“sentence pattern of function”. “Sentence pattern of function” has aEnglish grammar based style, and a sentence written in “sentence patternof function” is extremely readable for a man who can speak English. Thelexical definition of “sentence pattern of function” will be given laterin the present invention. As will be described later in the presentinvention, I call a sentence described in “sentence pattern offunction”, a ‘means for describing a function’ used as a rule‘. (See thelexical definition of’ means for describing a function used as a rule’given later in the present invention). According to this naming, thesame sentence I have stated just before is paraphrased as follows; Ifand when an object-oriented knowledge base system disclosed in thepresent invention is to be used to carry out an inference especially ina branch of software engineering, then, it is recommended that a “keydescribed using ‘means for describing a function used as a rule’” shouldbe used as a “key described using ‘means for storing data used asrules’” (See FIG. 5).

[1271] If and when an object-oriented knowledge base system disclosed inthe present invention has been used to give an answer to a questionasked by users of the system, and a good result is obtained finally,then, it is recommended that the log of the steps of the reasoning thatgave the answer successfully, should be recorded. It is recommended thecleverly arranged rules used in each step of the reasoning stored in thelog should be reused afterwards when similar question is to be answeredby using an object-oriented knowledge base system disclosed in thepresent invention. It is recommended that such a log should be recordedas a sentence described in “sentence pattern of instances of solvingproblems”. A plurality of rules described either in “sentence pattern ofphysical and/or mathematical rules” and/or in “sentence pattern offunction” may be used as a contents of a sentence described in “sentencepattern of instances of solving problems”. The lexical definition of“sentence pattern of instances of solving problems” will be given laterin the present invention. As will be described later in the presentinvention, I call a sentence described in “sentence pattern of instancesof solving problems”, a ‘means for storing data about instances ofsolving problems’. According to this naming, the same sentence I havestated just before is paraphrased as follows; If and when anobject-oriented knowledge base system disclosed in the present inventionhas been used to give an answer to a question asked by users of thesystem, and a good result is obtained finally, then, it is recommendedthat a “key described using ‘means for storing data used as rules’”which stores the body of the information of the log of the inference,should be described as “a key described according to ‘means for storingdata about instances of solving problems’ (See FIG. 5).

[1272] The second component of an ‘object-oriented knowledge base’ is“keys described using ‘means for storing data providing the ability ofassociation’” (See FIG. 1). A “key described using ‘means for storingdata providing the ability of association’” is a sentence which is usedas the basis of knowledge on which ‘means for carrying out an inference’helps the user to search ‘key words’ which are associated with naturalwords representing the user's idea. It is recommended that a user of anobject-oriented knowledge base system disclosed in the present inventionshould use a ‘key word’ rather than a ‘natural word’ when the useroperates the digital computing system. These ‘key words’ are usuallyused to make a query when the user wants to retrieve usable ‘rules’ fora step of reasoning during an inference. In an object-oriented knowledgebase system disclosed in the present invention, it is recommended that a‘descriptor’, and/or a ‘name-of-classification-item’ should be used as a‘key word’. The lexical definition of ‘means for storing data providingthe ability of association’, ‘descriptor’, and/or‘name-of-classification-item’ has been already given in the presentinvention.

[1273] The third component of an ‘object-oriented knowledge base’ is‘facts’ (See FIG. 1).

Lexical Definition of Facts

[1274] A sentence that is described in a form of a categoricalproposition is a fact if it is true. It is recommended that the body ofinformation of an ‘ideal thesaurus’ and/or of an ‘ideal classificationtable’ should be used as ‘facts’, in an object-oriented knowledge basesystem disclosed in the present invention (see FIG. 1).

[1275] It is recommended that a plurality of facts should be stored inan ‘ideal thesaurus’, and/or in an ‘ideal classification table’ in anobject-oriented knowledge base system disclosed in the presentinvention. In other words, an ‘ideal thesaurus’, and/or an ‘idealclassification table’ is a set of facts. Usually, a large number ofnouns are registered in an ‘ideal thesaurus’. And it is recommended thatthe information of the hierarchical relation among the nouns should bestored in the ‘ideal thesaurus’. And usually, a large number of verbsare registered in a ‘classification table’. And it is recommended thatthe information of the hierarchical relation among verbs should bestored in the ‘classification table’.

Lexical Definition of an ‘Object-Oriented Knowledge Base ManagementSystem’

[1276] An ‘object-oriented knowledge base management system’ is a systemwith which a maker of an object-oriented knowledge base system managesan ‘object-oriented knowledge base’.

[1277] An ‘object-oriented knowledge base management system’ is a partof an ‘object-oriented knowledge base system’ whose constitution is arecommended constitution. (See FIG. 1).

[1278] It is recommended that an ‘object-oriented knowledge basemanagement system’ disclosed in the present invention should comprise

[1279] 1) ‘ideal dictionary’

[1280] 2) ‘Means for giving definition of broader descriptor andnarrower descriptor’

[1281] and

[1282] 3) ‘Means for giving definition of higher classalgorithm-of-process and lower class algorithm-of-process’.

[1283] (See FIG. 6)

[1284] The lexical definition of an ‘ideal dictionary’ has already givenin the present invention. An ‘ideal dictionary’ is used, in anobject-oriented knowledge base system disclosed in the presentinvention, as the basis on which an ‘ideal thesaurus’ is constructed.That is, A‘means for giving definition of broader descriptor andnarrower descriptor’ is used as a tool to construct a hierarchicalsystem of nouns in an ‘ideal thesaurus’ on the basis of the‘object-oriented-lexical-definition of nouns’ in an ideal dictionary.

[1285] An ‘ideal dictionary’ is used also as the basis on which an‘classification table’ is constructed. That is, a ‘means for givingdefinition of higher class algorithm-of-process and lower classalgorithm-of-process’ is used as a tool to construct a hierarchicalsystem of verbs in an ‘ideal classification table’ on the basis of‘dichotomy of ‘quality”, in combination with c-language-like way ofdescription of English sentences in the lexicon’ described in an ‘idealdictionary’.

3.3.1. Making Full use of Hierarchy of ‘Descriptors’ and‘Names-of-Classification-Items’ in an Object-Oriented Database

[1286] As described above, both ‘descriptors’ and‘names-of-classification-items’ have hierarchy in an object orientedknowledge base system in the present invention. First, I describe how tomake the query for precise and specific retrieval in one case and how tomake the query for exhaustive retrieval in another case when an objectoriented knowledge base system disclosed in the present invention isused as a database. As a matter of fact, if and when a ‘rule’ isretrieved during an ‘inference’ carried out by an object-orientedknowledge base system disclosed in the present invention, then, theobject-oriented knowledge base system of the present system often worksjust as a similar way with which a database works. The detail how anobject-oriented knowledge base system disclosed in the present inventionworks will be disclosed later in the present invention. I claim that itis very effective to make use of hierarchy of ‘descriptors’ and‘names-of-classification-items’ in a database.

[1287] As mentioned before in the present invention, the hierarchicalstructure of ‘descriptors’ in the present invention is determined on thebasis of the idea of ‘object’. In this sense, a database disclosed inthe present invention is an object-oriented database.

[1288] Let me give some example in which an object-oriented knowledgebase is used, before I give detailed explanation about object-orientedknowledge base system. For example, let us assume that a user of thesystem tries to retrieve the information concerning ‘child raising ofpolar bears’. Let us assume that the user of the system first searchesappropriate ‘descriptors’ and/or ‘names-of-classification-items’. Let usassume that the user of the system first hits upon a ‘descriptor’_(bear)_ during a retrieval using a natural word ‘bear’ in the query onthe basis of @[algorithm of making a list of ‘descriptors’ ranked inorder of hit frequency]. And then, the user of the system hits upon the‘names-of-classification-items’ _{child raising}_ during a retrievalusing a natural word ‘raising’ in the query on the basis of @[algorithmof making a list of ‘names-of-classification-items’ ranked in order ofhit frequency].

[1289] As mentioned before, @[algorithm of making a list of‘descriptors’ ranked in order of hit frequency] is an algorithm withwhich one can obtain a list of ‘descriptors’ associated with a naturalword. And @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] is analgorithm with which one can obtain a list of‘names-of-classification-items’ associated with a natural word.

[1290] If and when, too many keys are obtained during a retrieval of theinformation concerning ‘child raising of polar bears’, using theintersection of _(bear)_ and _{child raising}_, then, it is recommendedthat the user of the system should consults the ‘ideal thesaurus’ tofind ‘descriptors’ which are narrower than _(bear)_, and should consultthe ‘ideal classification table’ to find ‘name-of-classification-item’which are lower than _{child raising}_.

[1291] It should be noted that in a usual database system, as well as inan object-oriented knowledge base system disclosed in the presentinvention, if a user of the system makes a query using a broader‘descriptor’, then, not only the keys including the broader ‘descriptor’but also the keys including any of the narrower ‘descriptors’ of thebroader ‘descriptor’, are retrieved. Therefore, if one wants a moreprecise retrieval, then, he should use a as narrow ‘descriptor’ aspossible.

[1292] It should also be noted that in a usual database system, as wellas an object-oriented knowledge base system disclosed in the presentinvention, if a user of the system makes a query on the basis of ahigher class ‘name-of-classification-item’, then, not only the keysincluding the higher class ‘name-of-classification-item’ but also thekeys including any of the lower class ‘names-of-classification-items’ ofthe higher class ‘names-of-classification-items’, are retrieved.Therefore, if one wants a more precise retrieval, then he should use aslow class ‘names-of-classification-items’ as possible.

[1293] Let us assume that the user of the system uses the ‘idealthesaurus’ and finds a ‘descriptor’ _(polar bear)_, which is a narrowerdescriptor of _(bear)_. This (polar bear)_ just fits to the retrieval ofthe theme. The user of the system will be able to make a retrieval moreprecisely by using a Boolean search in which the intersection of _(polarbear)_ and _{child raising}_ are used as a query, and the number of hitkeys will be reduced reasonably.

[1294] Thus, in general, if and when too many keys are retrieved usingbroader ‘descriptors’ in the query, then, it is recommended that a userof the database system should retrieve more precisely by using narrower‘descriptors’, instead of by using the broader ‘descriptors’. In such acase, it is recommended that the user should look up keys described in“sentence pattern of ‘ideal thesaurus’” to find such narrower‘descriptors’ associated with the broader ‘descriptors’. If and when auser of the system wants to, thus, find narrower ‘descriptors’, then itis recommended that he should make use of the procedure outlined inFormula. 13. In Formula. 13, a quasi-C code which outlines the procedureto search and list the ‘descriptors’ that are narrower (by one rank)than a ‘descriptor’ is shown.

[1295] And if and when too many keys are retrieved for a retrieval usinghigher class ‘names-of-classification-items’, then, it is recommendedthat a user of the database system should retrieve more precisely byusing lower class ‘names-of-classification-items’ instead of by usinghigher class ‘names-of-classification-items’. In such a case, it isrecommended that the user looks up keys described in “sentence patternof classification” to find such lower class‘names-of-classification-items’ associated with the higher class‘names-of-classification-items’. When a user of the system wants to,thus, find lower class ‘names-of-classification-items’, then it isrecommended that he should make use of the procedure outlined inFormula. 1 4. In Formula. 1 4, a quasi-C code which outlines theprocedure to search and list the ‘names-of-classification-items’ thatare lower class (by one rank) of a ‘name-of-classification-item’.

[1296] In general, the full purpose of retrieval made by the user of thesystem is seldom achieved only by carrying out single step of retrieval,but is usually approached step by step toward the perfect solution. Inother words, in a retrieval, the keys retrieved in the present step isused as seeds to search the key words to be used in the next step ofretrieval, and a retrieval using the key words should be practiced inthe next step.

[1297] In some cases of retrieval, too many keys are retrieved in thepresent step, and as a result, the processing capacity of the computersystem falls into puncture during the next step and/or during thereaftersteps. Combinatorial explosion described later is such a case.

[1298] In such cases, it is recommended that Method-1) described belowand/or Method-2) described in the next section should be carried out.

3.3.1.1. @[Algorithm of Narrowing Down the Target ‘Descriptors’ and/orTarget ‘Names-of-Classification-Items’]

[1299] I will give here a lexical definition of @[algorithm of narrowingdown the target ‘descriptors’ and/or target‘names-of-classification-items’].

[1300] <<Lexical Definition of @[algorithm of narrowing down the target‘descriptors’ and/or target ‘names-of-classification-items’]>> Method-i)defined below is an @[algorithm of narrowing down the target‘descriptors’ and/or target ‘names-of-classification-items’];

[1301] Method-1) is a method to make

retrieval to be made in the present step

more precise than

retrieval made in the preceding step

,

[1302] by using

[1303] not only the ‘descriptors’, the ‘next-best-natural-nouns’, the‘names-of-classification-items’ and the ‘next-best-natural-verbs’ thatwere used to form a conjunctive expression of a Boolean search's queryfor

retrieval made in the preceding step

,

[1304] but also supplemental ‘descriptors’, supplemental‘next-best-natural-nouns’, supplemental ‘names-of-classification-items’,and supplemental ‘next-best-natural-verbs’ that are contrived and/orrevived by the user of the system in the present step,

[1305] to form conjunctive expression of a Boolean search's query forthe

retrieval to be made in the present step

,

[1306] and/or,

[1307] by finding redundant ‘descriptors’, redundant‘next-best-natural-nouns’, redundant ‘names-of-classification-items’,and redundant ‘next-best-natural-verbs’ that were used to form adisjunctive expression of a Boolean search's query for

retrieval made in the preceding step

,

[1308] and omitting the redundant ‘descriptors’, the redundant‘next-best-natural-nouns’, the redundant‘names-of-classification-items’, and the redundant‘next-best-natural-verbs’ from disjunctive expressions of a Booleansearch's query for the

retrieval to be made in the present step

,

[1309] and/or,

[1310] by finding too broad ‘descriptors’ that were used to form aconjunctive and/or subjunctive expression of a Boolean search's queryfor T retrieval made in the preceding step] , and replacing them withtheir narrower ‘descriptors’, which can be found by looking up keysdescribed in “sentence pattern of ‘ideal thesaurus’” according to aprocedure such as the procedure outlined in Formula. 13, and using thenarrower ‘descriptors’ to form a conjunctive and/or subjunctiveexpression of a Boolean search's query for

retrieval to be made in the present step

,

[1311] and or,

[1312] by finding too high class ‘names-of-classification-items’ thatwere used to form a conjunctive and/or subjunctive expression of aBoolean search's query for

retrieval made in the preceding step

, and replacing them with their lower class‘names-of-classification-items’, which can be found by looking up keysdescribed in “sentence pattern of classification” according to aprocedure such as the procedure outlined in Formula. 14, and using thelower class ‘names-of-classification-items’ to form a conjunctive and/orsubjunctive expression of a Boolean search's query for

retrieval to be made in the present step

.

Lexical Definition of ‘Means for Narrowing Down the Target‘descriptors’” Lexical Definition of ‘Means for Narrowing Down theTarget ‘Names-of-Classification-Items”

[1313] @[Algorithm of narrowing down the target ‘descriptors’ and/ortarget ‘names-of-classification-items’] and/or something that stores theinformation of it, is a ‘means for narrowing down the target‘descriptors” when ‘descriptors’ are targeted in the algorithm, and is a‘means for narrowing down the target ‘names-of-classification-items”when ‘names-of-classification-items’ are targeted in the algorithm.

Lexical Definition of ‘Algorithm of Narrowing Down the Target‘Descriptors”

[1314] <<Lexical Definition of ‘algorithm of narrowing down the target‘names-of-classification-items”>> ‘Algorithm of narrowing down thetarget ‘descriptors” plus ‘algorithm of narrowing down the target‘names-of-classification-items” equals to @[algorithm of narrowing downthe target ‘descriptors’ and/or target ‘names-of-classification-items’]

3.3.1.2. @[Algorithm of Broadening Out the Target ‘Descriptors’ and/orTarget ‘Names-of-Classification-Items’]

[1315] On the other hand, for example, let me assume a situation inwhich so small number of keys are retrieved in the present step ofretrieval using the descriptor _(bear)_, that the retrieval in the nextstep can not be carried out any more.

[1316] In this situation, it is recommended that the user of anobject-oriented knowledge base system of the present invention shoulduse _(mammal)_, which is a broader ‘descriptor’ of _(bear)_, instead of_(bear)_to make a query. If the user of the system uses the descriptor_(mammal)_, then all the keys that include any of the narrower‘descriptors’ of _(mammal)_, such as _(cat)_, _(dog)_, _(panda)_, areretrieved by a database system. Therefore, much more number of keys areretrieved when _(mammal)_ is used instead of _(bear)_. Thus, the numberof retrieved keys can be increased.

[1317] In general, if and when too less keys are retrieved, then, it isrecommended that a user of an object-oriented knowledge base systemdisclosed in the present invention should make more exhaustive retrieveby making use of what I call @[algorithm of broadening out the target‘descriptors’ and/or target ‘names-of-classification-items’].

[1318] <<Lexical Definition of @[algorithm of broadening out the target‘descriptors’ and/or target ‘names-of-classification-items’]>> A methodto make

retrieval to be made in the present step

more exhaustive than

retrieval made in the preceding step

[1319] by using

[1320] not only the ‘descriptors’, the ‘next-best-natural-nouns’, the‘names-of-classification-items’ and the ‘next-best-natural-verbs’ thatwere used to form a disjunctive expression of a Boolean search's queryfor

retrieval made in the preceding step

,

[1321] but also supplemental ‘descriptors’, supplemental‘next-best-natural-nouns’, supplemental ‘names-of-classification-items’,and supplemental ‘next-best-natural-verbs’ that are contrived and/orrevived by the user of the system in the present step,

[1322] to form disjunctive expression of a Boolean search's query forthe

retrieval to be made in the present step

,

[1323] and/or,

[1324] by finding less important ‘descriptors’, less important‘next-best-natural-nouns’, less important‘names-of-classification-items’, and less important‘next-best-natural-verbs’ that were used to form a conjunctiveexpression of a Boolean search's query for

retrieval made in the preceding step

,

[1325] and omitting the less important ‘descriptors’, the less important‘next-best-natural-nouns’, the less important‘names-of-classification-items’, and the less important‘next-best-natural-verbs’ from conjunctive expressions of a Booleansearch's query for the

retrieval to be made in the present step

,

[1326] and/or,

[1327] by finding too narrow ‘descriptors’ that were used to form aconjunctive and/or subjunctive expression of a Boolean search's queryfor

retrieval made in the preceding step

, and replacing them with their broader ‘descriptors’, which can befound by looking up keys in “sentence pattern of ‘ideal thesaurus’”according to a procedure, such as a procedure outlined in Formula. 10,and using the broader ‘descriptors’ to form a conjunctive and/orsubjunctive expression of a Boolean search's query for

retrieval to be made in the present step

,

[1328] and or,

[1329] by finding too low class ‘names-of-classification-items’ thatwere used to form a conjunctive and/or subjunctive expression of aBoolean search's query for

retrieval made in the preceding step

, and replacing them with their higher class‘names-of-classification-items’, which can be found by looking up keysdescribed in “sentence pattern of classification” according to aprocedure, such as the procedure outlined in Formula. 11, and using thehigher class ‘names-of-classification-items’ to form a conjunctiveand/or subjunctive expression of a Boolean search's. query for

retrieval to be made in the present step

,

[1330] is an @[algorithm of broadening out the target ‘descriptors’and/or target ‘names-of-classification-items’].

Lexical Definition of ‘Means for Broadening Out the Target ‘descriptors”Lexical Definition of ‘Means for Broadening Out the Target‘Names-of-Classification-Items”

[1331] @[Algorithm of broadening out the target ‘descriptors’ and/ortarget ‘names-of-classification-items’] and/or something that stores theinformation of it, is a ‘means for broadening out the target‘descriptors” when ‘descriptors’ are targeted in the algorithm, and is a‘means for broadening out the target ‘names-of-classification-items”when ‘names-of-classification-items’ is targeted in the algorithm.

3.3.1.3. @[Algorithm of Fusing Propositions]

[1332] A step of reasoning carried out in an object-oriented knowledgebase system disclosed in the present invention is either a step offorward reasoning and/or a step of backward reasoning. Whether a forwardreasoning is carried out and/or a backward reasoning is carried out isdetermined is case by case. In a word, a step of reasoning carried outin an object-oriented knowledge base system disclosed in the presentinvention is a step of opportunistic reasoning. In a step of theopportunistic reasoning carried out by an object-oriented knowledge basesystem disclosed in the present invention, the rules to be used in theopportunistic reasoning are retrieved from the knowledge base. In aword, in the step, the object-oriented knowledge base system disclosedin the present invention works as a database system. In the followingdiscussion of the present section, only the case in which all the keysretrieved and/or composed in the present step of opportunistic reasoningare hypothetical propositions (i.e. are propositions in the form of “If**, then ***.”) is discussed. I call here, the ‘**’ the presuppositionof the hypothetical proposition, and I call the ‘***’ the consequence ofthe hypothetical proposition.

[1333] In some cases, so many rules which is a ‘hypothetical propositionto be used in forward reasoning’ and/or is a ‘hypothetical propositionsto be used in backward reasoning’ are retrieved, that the processingcapacity of the computer system falls into puncture during the next stepand/or during thereafter steps; i.e. I describe such situations that“combinatorial explosion occurs”.

[1334] In such a case, Method-1) can be used to cut down the number ofthe retrieved rules. However, as a side effect of Method-1), even auseful rule may be mistaken as a noise, and is abandoned. If a usefulrule is thus abandoned, then, the opportunistic reasoning may fall intoa deadlock in later steps. In such a case, Method-1) which caused such adeadlock must be withdrawn, and Method-2) should be used instead in thestep of the opportunistic reasoning of the present step.

[1335] Method-2) is used in an object-oriented knowledge base systemdisclosed in the present invention when combinatorial explosion occurs.In Method-2), too many ‘hypothetical propositions to be used in forwardreasoning’ and/or too many ‘hypothetical propositions to be used inbackward reasoning’ are ‘summarized’, in an rough and approximate way,into little number of general hypothetical propositions (i.e. littlenumber of general rules) with high degree of abstraction. A generalrule, thus obtained, of course, have many exceptions. Therefore, it isrecommended that verification should be done afterwards when an‘approximate’ solution is obtained in some later step of opportunisticreasoning, if and when this general and approximate rule is used. But asthe number of such general rule is very small, the computer systemevades puncture at the cost of some degree of incorrectness. Method-2)is a systematic way to make ‘random guesswork’ by using such generalrules. As such summarization is not exact, I use a word ‘fuse’ insteadof ‘summarize’ in the following discussion when I state this issue.

[1336] I will outline the concept of ‘fuse’ as follows:

[1337] Let me think about a situation in which there is one‘hypothetical proposition that is the target of the present step offorward reasoning’, and there are two hypothetical propositions to beused in the present step of forward reasoning. I call here, aproposition that is tried to be proven in the present step of forwardreasoning, the ‘hypothetical proposition that is the target of thepresent step of forward reasoning’. Under these assumptions, in a usualcase, two hypothetical propositions that are to be used as the target ofthe next step of opportunistic reasoning (i.e. there will be towhypothetical propositions that are tried to be proven in the next stepof opportunistic reasoning) will be obtained.

Lexical Definition of Opportunistic Reasoning

[1338] Inference in either step of which, either forward reasoningand/or backward reasoning is chosen and carried out case by case, is anopportunistic reasoning. In the present invention, I regard inference inwhich only forward reasoning is carried out in each step also as anopportunistic reasoning. I regard inference in which only backwardreasoning is carried out in each step also as an opportunisticreasoning.

[1339] However, there exists an exceptional case; If the twohypothetical proposition to be used in the present step of forwardreasoning has the same consequence, there will be only one hypotheticalpropositions which are the target of the next step of opportunisticreasoning, because only one reasoning is necessary in this case. In aword, two hypothetical proposition to be used in the present step offorward reasoning works as if they were the same hypotheticalproposition to be used in the present step of forward reasoning, if andwhen they have strictly the same consequence. I call this situation that“the two hypothetical proposition to be used in the present step offorward reasoning is strictly fused if and when they have strictly thesame consequence”. In this case, the number of the hypotheticalpropositions to be used in the present step of forward reasoning iseffectively and exactly one.

[1340] The main issue which I will describe in detail below is how tohandle the situation in which two or more‘hypothetical propositions tobe used in the present step of forward reasoning have approximately thesame consequence. In the following discussion, I will show @[algorithmof fusing propositions] by which to give a proposition which isapproximately equivalent to all of the consequences which are similarone another. I call this proposition the

fused consequence

.

[1341] And I call the hypothetical proposition

[1342] whose consequence is the

fused consequence

and whose presupposition equals to one of the presuppositions of the‘two or more hypothetical propositions to be used in the present step offorward reasoning’

[1343] the

fused hypothetical propositions to be used in the present step offorward reasoning

.

[1344] If there is one hypothetical proposition which is the target ofthe present step of forward reasoning and if the ‘two or morehypothetical proposition to be used in the present step of forwardreasoning’ are used in the present step of forward reasoning, then, twohypothetical propositions which are to be used as the target of the nextstep of opportunistic reasoning will be obtained. However, in@[algorithm of fusing propositions], a

fused hypothetical propositions to be used in the present step offorward reasoning

instead of‘two or more hypothetical proposition to be used in thepresent step of forward reasoning’ is used in the present step offorward reasoning. Then, as a result, only one approximate hypotheticalproposition that is the target of the next step of opportunisticreasoning will be obtained. In this case, the number of the hypotheticalpropositions to be used in the present step of forward reasoning iseffectively and approximately one.

[1345] In a word, in a procedure according to @[algorithm of fusingpropositions],

fused hypothetical propositions to be used in the present step offorward reasoning

is used. Then, as the result, exactness of the forward reasoning islost, but the number of ‘hypothetical propositions that are the targetof the next step of opportunistic reasoning’ is thus drastically reducedand the combinatorial explosion is expected to be avoided.

[1346] In the similar way, let me think about a situation in which thereis one hypothetical proposition that is the target of the present stepof backward reasoning, and there are two hypothetical proposition to beused in the present step of backward reasoning. I call here, aproposition that is tried to be proven in the present step of backwardreasoning, the ‘hypothetical proposition that is the target of thepresent step of backward reasoning’. Under these assumptions, in a usualcase, tow hypothetical propositions that are the target of the next stepof opportunistic reasoning will be obtained.

[1347] However, there exists an exceptional case; If the twohypothetical proposition to be used in the present step of backwardreasoning has the same presupposition, there will be only onehypothetical propositions which are the target of the next step ofopportunistic reasoning, because only one reasoning is necessary in thiscase. In a word, two hypothetical proposition to be used in the presentstep of backward reasoning works as if they were the same hypotheticalproposition to be used in the present step of backward reasoning, if andwhen they have strictly the same presupposition. I call this situationthat “the two hypothetical proposition to be used in the present step ofbackward reasoning is strictly fused if and when they have strictly thesame presupposition”. In this case, the number of the hypotheticalpropositions to be used in the present step of forward reasoning iseffectively and exactly one.

[1348] The main issue which I will describe in detail below is how tohandle the situation in which two or more hypothetical propositions tobe used in the present step of backward reasoning have approximately thesame presupposition. In the following discussion, I will show@[algorithm of fusing propositions] by which to give a proposition whichis approximately equivalent to all of the presuppositions which aresimilar one another. I call this proposition the

fused presupposition

, and I call the hypothetical proposition whose presupposition is the

fused presupposition

and whose consequence equals to one of the consequences of the ‘two ormore hypothetical propositions to be used in the present step ofbackward reasoning’ the

fused hypothetical propositions to be used in the present step ofbackward reasoning

.

[1349] If there is one hypothetical proposition which is the target ofthe present step of backward reasoning and if the ‘two or morehypothetical proposition to be used in the present step of backwardreasoning’ is used in the present step of backward reasoning, then,there will be more than two hypothetical propositions which are thetarget of the next step of opportunistic reasoning. However, in@[algorithm of fusing propositions], a

fused hypothetical propositions to be used in the present step ofbackward reasoning

instead of ‘two or more hypothetical proposition to be used in thepresent step of backward reasoning’ is used in the present step ofbackward reasoning, then, there will be only one approximatehypothetical propositions which are the target of the next step ofopportunistic reasoning. In this case, the number of the hypotheticalpropositions to be used in the present step of backward reasoning iseffectively and approximately one.

[1350] In a word, in a procedure according to @[algorithm of fusingpropositions],

fused hypothetical propositions to be used in the present step ofbackward reasoning

is used, then, as a result, exactness of the backward reasoning is lost,but the number of ‘hypothetical propositions which are the target of thenext step of opportunistic reasoning’ is thus drastically reduced andthe combinatorial explosion may be avoided.

[1351] Thus, Method-2) is a special kind of method to help a reasoning.More strictly speaking, Method-2) is a systemized way of ‘randomguesswork’ used in a reasoning of an object-oriented knowledge basesystem disclosed in the present invention. More specifically speaking,if and when Method-2) is used to get the answerer to a question justanyway, then, afterwards, the answer must be verified in a strict way.Method-2) is a method which is recommended to be carried out whenMethod- 1) can not be carried out and/or when too many keys areretrieved even after Method-1) has been carried out.

[1352] If a forward reasoning is carried out in the present step ofopportunistic reasoning, then the

propositions which should be fused

are the consequences of the ‘hypothetical proposition to be used inforward reasoning’, which are retrieved in the 20 present step ofopportunistic reasoning. And if a backward reasoning is carried out inthe present step of opportunistic reasoning, then the

propositions which should be fused

are the presuppositions of the ‘hypothetical proposition to be used inbackward reasoning’, which are retrieved in the present step ofopportunistic reasoning.

[1353] <<Lexical Definition of @[algorithm of fusing propositions]>>Method-2) defined below is an

[1354] @[algorithm of fusing propositions].

[1355] I will describe details of Method-2) carried out under such idealcases described below:

[1356] In ideal cases, a maker of the contents of an ‘object-orientedknowledge base’ disclosed in the present invention uses a‘classification table’ and have already classified verbs in the keysretrieved in the present step of opportunistic reasoning. And in idealcase, a maker of the contents of the ‘object-oriented knowledge base’disclosed in the present invention, have formalized the presuppositionsand the consequences of the hypothetical propositions that are used asrules, by using the five basic sentence patterns in English grammar.That is, for example, not only when the sentence of presuppositionand/or of consequence, is written in English sentences, but also whenthe sentence of presupposition and/or of consequence, is written innon-English sentences, the maker of the contents of an object-orienteddatabase disclosed in the present invention is recommended to divide thesentence of presupposition and/or of consequence into part of‘subject-word (S)’, part of ‘verb (V)’, part of ‘complement-word (C)’,part of ‘object-word (O)’, part of ‘indirect-object-word (I.O)’, andpart of ‘direct-object-word (D.O)’, explicitly according to the Englishgrammar. And then, to rearrange them according to the word order definedby English grammar.

[1357] In an ideal case, all the names of the ‘algorithms-of-processes’used as ‘ideal verbs’ of the formalized keys retrieved in the presentstep of opportunistic reasoning are a ‘name-of-classification-item’.

[1358] In such ideal cases described above, Method-2) is described asfollows

[1359] 1) First, I here show how to classify the

propositions which should be fused

in a step of opportunistic reasoning into several sets. As I discusshere the ideal case, a verb used in the

propositions which should be fused

is a ‘name-of-classification-item’.

[1360] Let me suppose that there exist two such

propositions which should be fused

. If the ‘names-of-classification-items’ used in the two

propositions which should be fused

share one common higher class ‘name-of-classification-item’, then, Iregard the two

propositions which should be fused

as members of the same set characterized by the common higher class‘name-of-classification-item’. This set is a set of

propositions which should be fused

which is characterized by the common higher class‘names-of-classification-items’.

[1361] More than two

propositions which should be fused

may be included in such a set characterized by a common higher class‘name-of-classification-item’, if more than two

propositions which should be fused

shares the common higher class ‘name-of-classification-item’; In otherwords, the number of the members of such a set may be more than two. Ingeneral la set characterized by a common higher class.‘name-of-classification-item’ has very large number of members, if thecommon higher class ‘name-of-classification-item’ is a very high class‘name-of-classification-item’. And in general, a set characterized by acommon higher class ‘name-of-classification-item’ has rather smallnumber of members if the common higher class‘name-of-classification-item’ is a not very high class‘name-of-classification-item’.

[1362] If the procedure outlined by the quasi-C code given in Formula.11 is used, then, the ‘names-of-classification-items’ that are higherclass (by one rank) of a ‘names-of-classification-items’ are searchedand listed. If the procedure outlined by the quasi-C code given inFormula. 11 is continued to be used for each of the‘names-of-classification-items’ in the list, the complete list in whichall the ‘names-of-classification-items’ which are higher class of a‘names-of-classification-items’ are registered, can be obtained at last.If such a complete list is made for two ‘names-of-classification-items’,and all the members in the two complete list is compared and a common‘names-of-classification-item’ is found, then, the two‘names-of-classification-items’ is judged to share common higher class‘names-of-classification-item’. If no such common‘names-of-classification-item’ is found, then the two‘names-of-classification-items’ is judged to not share any common higherclass ‘names-of-classification-item’. In such comparison, strcmp( ),which is an ANSI C library function, can be used.

[1363] Thus, varieties of size of such a kind of set, of course, can bedefined if and when ‘names-of-classification-items’ with varieties ofdegrees of abstraction are used to characterize such sets.

[1364] If the set comprising

[1365] the ‘hypothetical proposition to be used in forward reasoning’and/or the ‘hypothetical propositions to be used in backward reasoning’that are retrieved in a step of opportunistic reasoning,

[1366] are regarded as the universal set,

[1367] then,

[1368] sub-sets can be defined using above described way.

[1369] That is,

[1370] when the universal set is characterized by a common higher class‘names-of-classification-item’, let us assume that there is anothercommon higher class ‘names-of-classification-item’ which is lower classof the ‘names-of-classification-item’ characterizing the universal set.If and when, a member of the universal set contains

propositions which should be fused

which is the member of the set characterized the another common higherclass ‘names-of-classification-item’, which is lower class of the‘names-of-classification-item’ characterizing the universal set,

[1371] then, a set that has such members is a sub-set characterized bythe common higher class ‘names-of-classification-item’.

[1372] The higher the degree of abstraction of the‘names-of-classification-item’ characterizing a sub-set is, the smallerthe number of such sub-sets becomes. On the other hand, the lower thedegree of abstraction of the higher class‘names-of-classification-items’ is, the larger the number of suchsubsets becomes. I define an ‘appropriate degree of abstraction’ as theleast degree of abstraction high enough to make the number of suchsub-sets and to prevent the combinatorial explosion. In an ideal case,such an ‘appropriate degree of abstraction’ exists. Thus sub-sets of

propositions which should be fused

is defined.

[1373] In an ideal case, each of ‘subject-word (S)’, ‘complement-word(C)’, ‘object-word (O)’, ‘indirect-object-word (I.O)’,‘direct-object-word (D.O)’ of each sub-set of

proposition which should be fused

consists only of single ‘descriptor’.

[1374] 2) Next, it is recommended that

[1375] for each of ‘descriptor’ used for ‘subject-word (S)’,‘descriptor’ used for ‘complement-word (C)’, ‘descriptor’ used for‘object-word (O)’, ‘descriptor’ used for ‘indirect-object-word (I.O)’,and ‘descriptor’ used for ‘direct-object-word (D.O)’ of each sub-set of

propositions which should be fused

,

[1376] a broader ‘descriptor’ commonly associated with the‘subject-words (S)’ in the sub-set, a broader ‘descriptor’ commonlyassociated with the ‘complement-words (C)’ in the sub-set, a broader‘descriptor’ commonly associated with the ‘object-words (O)’ in thesub-set, a broader ‘descriptor’ commonly associated with the‘indirect-objects (I.O)’ in the sub-set, and/or a broader ‘descriptor’commonly associated with the ‘direct-object-words (D.O)’ in the sub-setshould be searched.

[1377] In an ideal case, such broader ‘descriptors’ exist.

[1378] 3) Next, for all the

propositions which should be fused

in each sub-set, replace the ‘names-of-classification-items’ with abovementioned common higher class ‘names-of-classification-item’, which hasan ‘appropriate degree of abstraction’.

[1379] Next, for all the

propositions which should be fused

in each sub-set, replace each of

[1380] ‘descriptor’ originally used for ‘subject-word (S)’, ‘descriptor’originally used for ‘complement-word (C)’, ‘descriptor’ originally usedfor ‘object-word (O)’, ‘descriptor’ originally used for‘indirect-object-word (I.O)’, and ‘descriptor’ originally used for‘direct-object-word (D.O)’

[1381] with each of above mentioned

[1382] the broader ‘descriptor’ commonly used in the sub-set for‘subject-words (S)’, the broader ‘descriptor’ commonly used in thesub-set for ‘complement-words (C)’, the broader ‘descriptor’ commonlyused in the sub-set for ‘object-words (O)’, the broader ‘descriptor’commonly used in the sub-set for ‘indirect objects (I.O)’, and/or thebroader ‘descriptor’ commonly used in the sub-set for‘direct-object-words (D.O)’.

[1383] As a result of this replacement, all the

propositions which should be fused

in each sub-set are transformed into a common single proposition, whichI call a

fused propositions

with ‘appropriate degree of abstraction’.

[1384] In ideal cases, thus formalized

fused proposition

with ‘appropriate degree of abstraction’ is singly obtained to each ofthe sub-sets.

[1385] In a step of forward reasoning, this

fused propositions

is called a

fused consequence

, and

fused hypothetical propositions to be used in the present step offorward reasoning

is defined as a hypothetical proposition whose consequence is the

fused proposition

and whose presupposition equals to one of the presuppositions of the‘two or more hypothetical propositions to be used in the present step offorward reasoning’.

[1386] In a step of backward reasoning, this

fused propositions

is called the

fused presupposition

, and

fused hypothetical propositions to be used in the present step ofbackward reasoning

is defined as a hypothetical proposition whose presupposition is the

fused presupposition

and whose consequence equals to one of the consequences of the ‘two ormore hypothetical propositions to be used in the present step ofbackward reasoning’.

[1387] Thus formalized

fused hypothetical proposition to be used in the present step of forwardreasoning

and/or

fused hypothetical proposition to be used in the present step ofbackward reasoning

is the general rule of thumb with high degree of abstraction and withmany exceptions, as I have mentioned before.

Lexical Definition of ‘Means for Fusing Propositions’

[1388] @[Algorithm of fusing propositions] and/or something that storesthe information of it, is a ‘means for fusing propositions’.

[1389] Thus formalized hypothetical propositions can be regardedapproximately as a ‘large-grained rule’.

[1390] Combinatorial explosion can be avoided if none of the plenty ofnumbers of keys retrieved in the present step of opportunistic reasoningare used but adequately small number of approximate ‘large-grainedrules’ thus obtained are used instead.

[1391] Thus far, only ideal cases are described, but Method-2) isdefined even in non-ideal case as described below.

[1392] If the ‘names-of-classification-items’ used in the two

propositions which should be fused

do not share any common higher class ‘name-of-classification-item’, thena “higher class ‘names-of-classification-item’ approximately shared bythem” is recommended to be used.

[1393] Here, a “higher class ‘name-of-classification-item’ approximatelyshared by two hypothetical propositions” means

[1394] either

[1395] a ‘name-of-classification-item’ which is higher class of both

[1396] the ‘name-of-classification-item’ which is closely associatedwith the ‘natural-verb’used in one of the

propositions which should be fused

[1397] and the ‘name-of-classification-item’ which is closely associatedwith the ‘natural-verb’ used in the other of the

propositions which should be fused

[1398] and/or

[1399] a ‘name-of-classification-item’ closely associated with both

[1400] a higher class ‘names-of-classification-item’ of the‘name-of-classification-item’ used as the verb used in one of the

propositions which should be fused

[1401] and a higher class ‘names-of-classification-item’ of the‘name-of-classification-item’ used as the verb used in the

propositions which should be fused

.

[1402] Here, “closely associated” means that one of them is ranked highwhen @[algorithm of making a list of ‘names-of-classification-items’ranked in order of hit frequency] is carried out for the other.

[1403] Similarly, when no ‘broader ‘descriptors“is strictly shared bytwo corresponding ‘descriptors’ two

propositions which should be fused

in a sub-set’ dose not exist,

[1404] either

[1405] a ‘descriptor’ which is broader than both

[1406] the ‘descriptor’ which is closely associated with the‘natural-noun’ used as a noun in one of the

propositions which should be fused

[1407] and the ‘descriptor’ which is closely associated with the‘natural-noun’ used as a noun in the other

propositions which should be fused

[1408] or

[1409] a ‘descriptor’ closely associated with both

[1410] a broader ‘descriptor’ of the ‘descriptor’ used as the noun inone of the

propositions which should be fused

[1411] and a broader ‘descriptor’ of the ‘descriptor’ used as the nounin the other

propositions which should be fused

.

[1412] Here, ‘closely associated’ means ‘one is ranked high when@[algorithm of making a list of ‘descriptors’ ranked in order of hitfrequency] is carried out for the other’.

[1413] When no appropriate ‘descriptors’ and/or‘names-of-classification-items’ to be used are found, a user of thesystem must found at his discretion the ‘next-best-natural-nouns’ andthe ‘next-best-natural-verbs’ to be used.

[1414] When formalization using five basic sentence pattern isimpossible, it is recommended that a maker of the contents of a databasedisclosed in the present invention should use only the form of “If ˜,then ˜.”. That is, ‘subject-word (S)’, ‘verb (V)’, ‘complement-word(C)’, ‘object-word (O)’, ‘indirect-object (I.O)’, and‘direct-object-word (D.O)’ are not be distinguished each other. Instead,it is recommended that ‘Broader ‘descriptor’ strictly and/orapproximately shared by all the formalized keys of a classificationitem’ and/or ‘higher class ‘names-of-classification-items’ strictlyand/or approximately shared by the members of a classification item’should be simply arranged on ‘˜’ of ‘If ˜’, and/or on ‘˜’ of ‘then ˜’,and the form “If ˜, then ˜.” thus obtained is regarded as

fused hypothetical propositions to be used in the present step offorward reasoning

or

fused hypothetical propositions to be used in the present step ofbackward reasoning

.

[1415] In the case in which even this measure is impossible, it isrecommended that appropriate, ‘next-best-natural-noun’ and/or‘next-best-natural-verb’ are simply arranged on ‘˜’ of ‘If ˜’, and/or on‘˜’ of‘then ˜’, and the form “If ˜, then ˜.” thus obtained should beregarded as a formalized key.

3.3.2. @[Algorithm of Sentence Based Object-Oriented CategoricalSyllogism]

[1416] By the way, the following point should be noticed;

[1417] That is, as ‘mammal’ is a broader term of ‘rat’,

[1418] if the categorical proposition,

[1419] “any mammal drinks milk from its mother's body when it is young”is true, then the categorical proposition

[1420] “any rat drinks milk from its mother's body when it is young” isalso true.

[1421] This kind of rule is widely accepted and is called the “heritageof the attribute of objects”.

[1422] I disclose in the present invention that a similar but differentrule exists: As ‘move’ is a higher class ‘names-of-classification-items’of ‘walk’,

[1423] if the categorical proposition,

[1424] “any turtle walks”

[1425] is true, then the categorical proposition,

[1426] “any turtle moves”

[1427] is also true.

[1428] In the present invention, I claim that these are a kind ofcategorical syllogism, and I call such syllogism leading from onecategorical proposition to another categorical proposition, the

[1429] @[algorithm of sentence based object-oriented categoricalsyllogism].

Lexical Definition of @[Algorithm of Sentence Based Object-OrientedCategorical Syllogism]

[1430] I claim that in an ideal case, exact formalization of thedefinition of @[algorithm of sentence based object-oriented categoricalsyllogism], which is an algorithm with which a special kind ofcategorical syllogism is carried out on the basis of linguisticsentence, is given in a clear and explicit way in a proposition iswritten in English. In this special kind of categorical syllogism,hierarchical structures of nouns registered in the ‘ideal thesaurus’and/or hierarchical structure of verbs registered in the ‘classificationtable’ is made full use of. A sentence based object-oriented categoricalsyllogism, like a usual categorical syllogism, comprises, major premise,minor premise, and, conclusion. But the way in which this specialcategorical syllogism is embodied has several patterns, because, as iswell known, English sentence is classified into five basic sentencepatterns. I claim in the present invention that the @[algorithm ofsentence based object-oriented categorical syllogism] is defined foreach of the five basic sentence patterns as follows.

[1431] The @[algorithm of sentence based object-oriented categoricalsyllogism] for ‘form I’: ‘subject-word (S)’+‘verb (V)’,

[1432] is described as follows;

[1433] If

[1434] a categorical proposition written in a sentence, S+V, is true,(Major premise)

[1435] and

[1436] S′ is synonym and/or narrower ‘descriptor’ of S, (Minor premise)

[1437] then,

[1438] the categorical proposition written in the sentence, S′+V, isalso true. (Conclusion)

/* Example */

[1439] If

[1440] “Any bird flies”

[1441] is true,

[1442] then,

[1443] “A sparrow flies”

[1444] is true.

[1445] The @[algorithm of sentence based object-oriented categoricalsyllogism] for ‘form II’: ‘subject-word (S)’+‘verb (V)’+‘complement-word(C)’, is described as follows;

[1446] If

[1447] a categorical proposition written in a sentence, S+V+C, is true,(Major premise)

[1448] and

[1449] S′ is synonym and/or narrower ‘descriptor’ of S, (Minor premise),

[1450] and

[1451] C′ is synonym and/or broader ‘descriptor‘of C, (Minor premise),

[1452] then,

[1453] the categorical proposition written in the sentence, S′+V+C′, isalso true. (Conclusion)

[1454] This case is schematically shown in Formula. 1A.

/* Example */

[1455] If

[1456] “A cat is a mammal”

[1457] is true,

[1458] then, not only

[1459] “A Persian cat is a mammal”

[1460] is true, but also

[1461] “A cat is an animal”

[1462] is true.

[1463] The @[algorithm of sentence based object-oriented categoricalsyllogism] for ‘form III’: ‘subject-word (S)’+‘verb (V)’+‘object-word(O)’.

[1464] is described as follows;

[1465] If

[1466] a categorical proposition written in a sentence, S+V+O, is true,(Major premise)

[1467] and,

[1468] S′ is synonym and/or narrower ‘descriptor’ of S, (Minor premise)

[1469] and,

[1470] O′ is synonym and/or narrower ‘descriptor’ of O, (Minor premise)

[1471] then,

[1472] the categorical proposition written in the sentence, S′+V+O′, isalso true. (Conclusion)

[1473] This case is schematically shown in Formula. 1B.

/* Examples */

[1474] If,

[1475] “Any cat catches a mouse”

[1476] is true,

[1477] then,

[1478] not only

[1479] “A Persian cat catches a mouse”

[1480] is true, but also

[1481] “A cat catches a field mouse”

[1482] is also true.

[1483] The @[algorithm of sentence based object-oriented categoricalsyllogism] for ‘form IV’: ‘subject-word (S)’+‘verb(V)’+‘indirect-object-word (I.O)’+‘direct-object-word (D.O)’,

[1484] is described as follows;

[1485] If

[1486] a categorical proposition written in a sentence, S+V+I.O+D.O, istrue, (Major premise)

[1487] and,

[1488] S′ is synonym and/or narrower ‘descriptor’ of S, (Minor premise)

[1489] and,

[1490] I.O′ is synonym and/or narrower ‘descriptor’ of I.O, (Minorpremise)

[1491] and,

[1492] D.O′ is synonym and/or narrower ‘descriptor’ of D.O, (Minorpremise)

[1493] then,

[1494] the categorical proposition written in the sentence,S′+V′+I.O′+D.O′ is also true. (Conclusion)

/* Examples */

[1495] If a sentence

[1496] “Any drugstore sells a man drugs”

[1497] is true,

[1498] then,

[1499] “A drug store in Tokyo sells a man drugs”,

[1500] “Any drugstore sells an American drugs”

[1501] and

[1502] “Any drug store sells a man a medicine for cold”

[1503] are true.

[1504] The @[algorithm of sentence based object-oriented categoricalsyllogism] for ‘form V’: ‘subject-word (S)’+‘verb (V)’+‘object-word(O)’+‘complement-word (C)’, is described as follows;

[1505] If

[1506] a categorical proposition written in a sentence, S+V+O+C, istrue, (Major premise)

[1507] and,

[1508] S′ is synonym and/or narrower ‘descriptor’ of S, (Minor premise)

[1509] and,

[1510] O′ is synonym and/or narrower ‘descriptor’ of O, (Minor premise)

[1511] and,

[1512] C′ is synonym and/or broader ‘descriptor’ of C, (Minor premise)

[1513] then,

[1514] the categorical proposition written in the sentence, S′+V+O′+C′,is also true. (Conclusion)

/* Examples */

[1515] If

[1516] “Any man calls Newton a physicist”

[1517] is true,

[1518] then,

[1519] not only

[1520] “A Japanese calls Newton a physicist”,

[1521] but also

[1522] “A man calls Newton a scientist”

[1523] is true.

[1524] And

[1525] if

[1526] “Any man found any computer useful”

[1527] is true,

[1528] then,

[1529] not only

[1530] “An American found any computer useful”

[1531] but also

[1532] “Any man found a personal computer useful”

[1533] is true.

[1534] The @[algorithm of sentence based object-oriented categoricalsyllogism] for cases when the verb used in the major premise and theverb used in the conclusion is different,

[1535] is described as follows;

[1536] If

[1537] a categorical proposition PY that describes an‘name-of-classification-item’ as a ‘simple sentence including a wordused as an ‘ideal verb”, is true, (Major premise)

[1538] and,

[1539] PX is a higher class ‘name-of-classification-item’ of PY, and PXis also a ‘simple sentence including a word used as an ‘ideal verb”,(Minor premise)

[1540] then,

[1541] a categorical proposition which describes an‘algorithm-of-process’, PX, is true. (Conclusion)

[1542] About the definition of the term, higher class‘algorithm-of-process’, see the description which has already given byme at the issue of ‘lexical definition’ of @[algorithm of givingdefinition of higher class ‘algorithm-of-process’ and lower class‘algorithm-of-process’]. A quasi-C code which outlines the procedurewith which to judge whether PX is higher class‘name-of-classification-item’ of PY or not is shown in Formula. 12. /*Example */ If “A tortoise walks” is true, then, “A tortoise moves” isalso true. If “Tom proposed to Mary” is true, then, not only “Tome madea decision” but also “Tom contacted Mary” is true. And if “Rommel actedin Sahara” is true, then “if( (Rommel) acted strategically in Sahara) {(Rommel) intended the (aim) in Sahara ;_(—) (someone else) tried to makethe (strategy) in Sahara ;_(—) }” is also true.

[1543] The procedure outlined by a quasi-C code in Formula. 15A+Formula.15B+Formula. 15C+Formula. 15D, are a recommended embodiment of an@[algorithm of sentence based object-oriented categorical syllogism].More precisely speaking, with the procedure outlined in Formula.15A+Formula. 15B+Formula. 15C+Formula. 15D, one can judge whether aproposition is a conclusion for a major premise or not, if he specifythe thesaurus and the classification table which he uses.

Lexical Definition of ‘Means for Carrying Out Sentence BasedObject-Oriented Categorical Syllogism’

[1544] @[Algorithm of sentence based object-oriented categoricalsyllogism] and/or something that stores the information of it, is a‘means for carrying out sentence based object-oriented categoricalsyllogism’.

[1545] @[Algorithm of sentence based object-oriented categoricalsyllogism] is used as a mechanism of reasoning in an object-orientedknowledge base system disclosed in the preset invention.

[1546] For example, remember the sentence,

[1547] _ALGORITHM_ (someone) carries (something) {(someone) lifts(something);_(someone) takes (something);_}.

[1548] This sentence shows that either the sentences,

[1549] (someone) lifts (something),

[1550] and/or

[1551] (someone) takes (something),

[1552] is

[1553] higher class ‘algorithm-of-process’ of (someone) carries(something).

[1554] Therefore, according to my The @[algorithm of sentence basedobject-oriented categorical syllogism], if the sentence,

[1555] (someone) carries (something),

[1556] is true, then,

[1557] Either the sentences,

[1558] (someone) lifts (something),

[1559] and/or

[1560] (someone) takes (something),

[1561] is true.

[1562] Let me think about the “instance of a sentence “someone carriessomething”, which is described in “sentence pattern of implementation ofnames of algorithms-of-processes””,

[1563] _ALGORITHM_ (Tom) carries (a case of beers) {(Tom) lifts (a caseof beers);_(Tom) takes (a case of beers);_}.

[1564] As this “instance” ca be rewritten as

[1565] _ALGORITHM_ Tom carries a case of beers {someone=Tom;_something=a case of beer;_someone lifts something;_someone takessomething;_}.

[1566] by definition, the “instance” of “Someone carries something”,i.e., “Tom carries a case of beers” is, the name of a lower class of‘algorithm-of-process’ of the original ‘algorithm-of-process’ denoted bythe sentence, “(someone) carries (something)”.

[1567] Therefore, according to @[algorithm of sentence basedobject-oriented categorical syllogism], if the sentence,

[1568] “Tom carries a case of beer”,

[1569] is true,

[1570] the sentences,

[1571] “Someone carries something”,

[1572] is true. And of course, the sentences,

[1573] “(someone) lifts (something)”,

[1574] and,

[1575] “(someone) takes (something)”are also true.

[1576] because these sentences are the name of higher class‘algorithm-of-process’ of the ‘algorithm-of-process’ denoted by thesentence,

[1577] “Tom carries a case of beer”.

[1578] The outline of the algorithm with which whether PX is higherclass ‘name-of-classification-item’ of PY or not is given as a quasi-Ccode in Formula. 12. To avoid misjudge about whether higher class ornot, it is recommended that once a procedure is used to implement a‘name-of-classification-item’, then, the procedure should never usedagain in other procedures to implement other‘names-of-classification-items’. Only the name of an‘algorithm-of-process’, such as ‘name-of-classification-item’, not thebody of ‘algorithm-of-process’, may be reused to implement others.

[1579] Above definitions can be object-oriented to a complex sentence.That is, the previously mentioned syllogism can be applied to each ofthe nested structure of the sentence recursively.

[1580] I regard this syllogism for a complex sentence also as a kind ofthe @[algorithm of sentence based object-oriented categorical syllogism]

[1581] It should be noted that The @[algorithm of sentence basedobject-oriented categorical syllogism] is well defined only when thedeterminers ‘all’, ‘any’, and/or ‘every’ modifies them. If thesedeterminers are not used, exception to the exact applicability of@[algorithm of sentence based object-oriented categorical syllogism]arises. In such cases, @[algorithm of sentence based object-orientedcategorical syllogism] should be regarded as an approximate algorithm.These exceptions must be handled and verified afterwards by manual. Itis recommended to make an axiom system for frequently used set ofknowledge.

[1582] When @[algorithm of sentence based object-oriented categoricalsyllogism] is used, it is recommended that a sentences like,

[1583] B is synonym and/or broader ‘descriptor’ of A,

[1584] should be used rather than a sentence like

[1585] A is synonym and/or narrower ‘descriptor’ B,

[1586] because, as mentioned before, in general, it is much easy taskfor a computer to search and list all the broader ‘descriptors’ of a‘descriptor’ than to search and list all the narrower ‘descriptors’ of a‘descriptor’.

[1587] The procedure to search and list the ‘descriptors’ that arebroader (by one rank) than a ‘descriptor’ is outlined as a quasi-C codein Formula. 10. The procedure to search and list all the ‘descriptors’that are broader than a ‘descriptor’ can be obtained if the quasi-C codein Formula. 10 is used recursively and completely.

[1588] The procedure to search and list the‘names-of-classification-items’ that are higher class (by one rank) of a‘names-of-classification-items’ is outlined as a quasi-C code inFormula. 11. The procedure to search and list all the usable‘names-of-classification-items’ that are higher class of a‘names-of-classification-items’ can be obtained if the quasi-C code inFormula. 11 is used recursively.

[1589] That is, one may regard all the usable‘names-of-classification-items’ included in the list of the‘names-of-classification-items’ that are higher class (by one rank) of aseed ‘name-of-classification-item’, as another seed‘name-of-classification-item’. If this procedure is continued until nohigher class ‘name-of-classification-item’ can be listed any more, then,as the result, all the usable ‘name-of-classification-items’ that arehigher class of the usable ‘name-of-classification-items’ can beobtained.

[1590] Above discussion is about an ideal case. In the case when a‘natural-verb’ is used in a basic sentence pattern, it is recommendedthat the @[algorithm of making a list of ‘names-of-classification-items’ranked in order of hit frequency] should be used to get‘names-of-classification-items’ most closely associated to the‘natural-verb’. If an appropriate ‘name-of-classification-item’ arefound, a verb that is closely associated to a higher class‘names-of-classification-items’ of the “appropriate‘name-of-classification-item’” is approximately regarded to be thehigher class verb of the ‘natural verb’. And a verb that is closelyassociated to a lower class ‘names-of-classification-items’ of the“appropriate ‘name-of-classification-item’” is approximately regarded tobe the lower class verb of the ‘natural verb’.

[1591] When no appropriate ‘names-of-classification-items’ are obtained,the user of the system must find lower class and higher class verb, athis discretion.

[1592] In addition, in some realistic cases, ‘natural-nouns’ and/or‘natural noun’ phrases are used as ‘subject-word (S)’, ‘complement-word(C)’, ‘object-word (O)’, ‘indirect-object-word (I.O)’, and/or‘direct-object-word (D.O)’. In such cases, it is recommended that@[algorithm of making a list of ‘descriptors’ ranked in order of hitfrequency] should be used to get ‘descriptors’ most closely associatedto the ‘natural-nouns’. If an appropriate ‘descriptor’ are found, then,a term that is closely associated to a broader ‘descriptor’ of “theappropriate ‘descriptor’” is approximately regarded to be the broaderterm, and a term that is closely associated to a narrower ‘descriptor’of the “appropriate ‘descriptor’” is approximately regarded to be thenarrower term.

[1593] When no appropriate ‘descriptors’ are obtained, the user of thesystem must find lower and higher term at his discretion.

[1594] In English grammar, a sentence may be paraphrased in varieties ofways into different basic sentence patterns. As a result, thepresupposition and the consequence of an object-oriented categoricalsyllogism may be in different basic sentence patterns, in such cases. Inother words, two categorical propositions in different basic sentencepatterns may be mutually in the relation of presupposition andconsequence of an object-oriented categorical syllogism. Therefore, evenwhen two categorical propositions are in different basic sentencepatterns, they may be judged to be in the presupposition-consequencerelationship to each other in such cases. Therefore, if ‘descriptors’and/or ‘names-of-classification-items’ of two propositions are closelyassociated with each other, then, the two propositions may be in therelation of presupposition and consequence of an object-orientedcategorical syllogism with considerable probability. Whether they arestrictly in the presupposition-consequence relationship to each other ornot should be judged by the user of an object-oriented knowledge basesystem disclosed in the present invention at his discretion. In thepresent invention, such a procedure of judgment is also regarded as an@[algorithm of sentence based object-oriented categorical syllogism].

[1595] If one wants to let an object-oriented knowledge base systemdisclosed in the present invention make a perfectly full automatedinference, then, it is recommended that he should construct an axiomsystem to be used as the object-oriented knowledge base system. TheNewtonian mechanics is one of the most famous axiom systems. I will showlater in the present invention, how axioms of the Newtonian mechanicsare expressed using the way of knowledge representation of anobject-oriented knowledge base system disclosed in the presentinvention.

[1596] In some cases even when two categorical propositions are judgedprobably to be in the presupposition-consequence relationship to eachother but strictly not, it is recommended that the step of theopportunistic reasoning should be continued anyway to achieve a roughand approximate answer, and the strict verification should be done afterthe rough answer is obtained. If the rough answer is verified to bestrictly correct afterwards, then, the rough answer is judged to be thefinal answer. Else if the rough answer is judged not to be strictlycorrect, then, the rough answer should be revised.

[1597] So, I regard two categorical propositions judged probably to bein the presupposition-consequence relationship to each other butstrictly not, as also to be connected by @[algorithm of sentence basedobject-oriented categorical syllogism] in the present invention.

[1598] <<Lexical Definition of “sentence pattern of one of five basicsentence patterns of English grammar”>> As the data structurerecommended to be used to describe 5 sentences in “sentence pattern ofone of five basic sentence patterns of English grammar”, I disclose inthe present invention

[1599] SplusV _S=_*** _V=_***.

[1600] SplusVplusC_(— —)S=_*** _V=_*** _C=_***.

[1601] SplusVplusO_(— —)S=_*** _V=_*** _O=_***.

[1602] SplusVplusIOplusDO _S=_*** _V=_*** _IO=_*** _DO=_***.

[1603] SplusVplusOplusC _S=_*** _V=_*** _O=_*** _C=_***,

[1604] where,

[1605] S means ‘subject-word (S)’,

[1606] V means ‘verb (V)’,

[1607] C means ‘complement-word (C)’,

[1608] O means ‘object-word (O)’,

[1609] IO means ‘indirect-object-word (I.O)’,

[1610] DO means ‘direct-object-word (D.O)’.

[1611] These five data structures may be nested by each other.

Lexical Definition of Means for Describing Sentences According to aSimple English Grammar

[1612] A sentence in “sentence pattern of one of five basic sentencepatterns of English grammar” and/or something that stores theinformation of it, is a means for describing sentences according to asimple English grammar.

[1613] “Sentence pattern of one of five basic sentence patterns ofEnglish grammar” is of course a sentences described according to asimplified English grammar.

3.3.3. Sentence Used as ‘Rule’ Describing the Relation Between Cause andEffect

[1614] By the way, a hypothetical proposition can be formalized by usingthe form of “If ˜, then ˜” and categorical propositions used as ‘˜’s. Asa categorical proposition can be formalized by using “sentence patternof one of five basic sentence patterns of English grammar”, ahypothetical proposition can be formalized by using the form of “If ˜,then ˜” and “sentence pattern of one of five basic sentence patterns ofEnglish grammar”.

[1615] <<Lexical Definition of “sentences that store data used asrules”>> A sentences describing a rule used in a knowledge base is a“sentence that stores data used as rule”. It is recommended that“sentences that store data used as rules” should be described either asa sentences in “sentence pattern of physical and/or mathematical rules”and/or as a sentences in “sentence pattern of function”.

Lexical Definition of ‘Means for Storing Data Used as Rules’

[1616] “Sentence which stores data used as rules” and/or something thatstores the information of it, is a ‘means for storing data used asrules’.

[1617] A “sentence which store data used as rule” is a sentence used as‘rule’.

[1618] It is recommended that the ‘means for storing data used as rules’should be 20 described either as a sentences in “sentence pattern ofphysical and/or mathematical rules” and/or as a sentences in “sentencepattern of function”.

[1619] I first discuss keys recording rules describing the relationbetween cause and effect. In the present invention, I claim that, inideal cases, the rule that describes the relation between cause andeffect can be described as a sentence in the form of physical law and/orin the form of mathematical formula. Here, mathematical formula includesaxioms. These rules of physics and mathematics are often expressed byusing mathematical equation.

[1620] As will be discussed later in the present invention, I claim thatin almost all cases, physical laws and/or mathematical formula can bewritten in the form, “According to the law ***, if ***, then ***.”

[1621] <<Lexical Definition of “sentence pattern of physical and/ormathematical rules”>> Thus, I define here, “sentence pattern of physicaland/or mathematical rules”, which is suitable to describe keys recordingrules describing the relation between cause and effect as

[1622] “sentence pattern of physical and/or mathematical rules”:

[1623] _RULE_***** _states_if( **** )then{*** };_(—)

[1624] where ‘*****’ is the name of the rule, ‘****’ is thepresupposition , and ‘***’ is the conclusion.

Lexical Definition of ‘Means for Storing Data Used as Rules in a FormalWay’

[1625] A sentence in “sentence pattern of physical and/or mathematicalrules” and/or something that stores the information of it, is a ‘meansfor storing data used as rules in a formal way’.

[1626] This “sentence pattern of physical and/or mathematical rules” canbe used to represent the key of a data used in an object orientedknowledge base system of the present invention.

Lexical Definition of ‘Presupposition’ of a ‘Hypothetical Proposition’Described in “Sentence Pattern of Physical and/or Mathematical Rules”

[1627] Here, I define the lexical meaning of the term ‘presupposition’of a ‘hypothetical proposition’ described in “sentence pattern ofphysical and/or mathematical rules” here.

[1628] If and when the hypothetical proposition is described in“sentence pattern of physical and/or mathematical rules”,

[1629] _RULE_******* _states_if(******)then{*****};_,

[1630] then, the ‘presupposition’ of the ‘hypothetical proposition’ isthe proposition in the field below ‘_states_if(’. That is, the‘presupposition’ is ‘*****’.

Lexical Definition of ‘Consequence’ of a ‘Hypothetical Proposition’Described in “Sentence Pattern of Physical and/or Mathematical Rules”

[1631] And, if and when the hypothetical proposition is described in“sentence pattern of physical and/or mathematical rules”,

[1632] _RULE_******* _states_if(******)then{*****};_,

[1633] then, the ‘consequence’ of the ‘hypothetical proposition’ meansthe proposition in the field below ‘)then{’. That is, the ‘consequence’is ‘*****’.

3.3.4. Newton's Mechanics Represented Using a Knowledge RepresentationSystem of an Object-Oriented Knowledge Base System Disclosed in thePresent Invention

[1634] Let me show here an example in which the axiom system of Newton'smechanics is smartly described if my data structures including “sentencepattern of physical and/or mathematical rules”.

[1635] As a first step, let me give what Newton's equation of motion is,for a man who is not a physicist. Newton's equation of motion is a mostfamous physical law describing motion of macroscopic particles in theworld. Newton's equation of motion for a macroscopic particle is givenby

F=ma,

[1636] where ‘m’ is the mass of a ‘macroscopic particle’, ‘F’ is theexternal force exerted on the ‘macroscopic particle’, and ‘a’ is theacceleration of the ‘macroscopic particle’ under the force ‘F’.

[1637] I will give an explanation of this equation, ‘F=ma’ using anexample below. For the sake of simplicity, let me restrict my example inthe very simple case wherein the motion of a ‘macroscopic particle’along a ‘straight line’.

[1638] Let us give here an example of “motion of a ‘macroscopicparticle’ along a ‘straight line’”. Let us imagine that a fat guy, forexample, me, travels by a train from a station ‘A’ to a neighboringstation ‘B’, which is, say, 10000 meters (i.e. 6.22 miles) away from thestation ‘A’. Suppose that the fat guy travels by an imaginary idealtrain, which runs without any shake or vibration, even whileaccelerating and/or decelerating from time to time quite violently butstrictly along a ‘perfectly strait railroad track without any bump’.

[1639] As will be strictly explained later, this ‘perfectly straitrailroad track without any bump’ is a good model of the ‘straight line’,which I mentioned before. For the sake of simplicity, let us supposethat the ‘perfectly strait railroad track without any bump’ is built ona perfectly horizontal and flat ground. Here of course, the curvature ofthe surface of the globe is neglected.

[1640] Suppose that the fat guy sit on an idealized chair that facesjust the direction of the movement of the train. Suppose that the fourlegs of the ideal chair are fixed firmly to the floor of the idealtrain. For the sake of simplicity, let us suppose that the fat guy'slegs are hanging and his feet are floating in the air and never step onthe floor of the ideal train. Suppose that the surface of the idealchair is perfectly slippery. Suppose that the fat guy's body is fastenedto the back of the ideal chair with a seatbelt made of steel springs. Ina word, the body of the fat guy is supposed to be supported only by

[1641] 1) steel springs used as cushion of the seat of the ideal chair,

[1642] 2) steel springs used as the cushion of the back of the idealchair, and,

[1643] 3) the steel springs used as the seatbelt of the ideal chair.

[1644] Suppose that the fat guy sit on the ideal chair with perfectlygood manner without budging any part of his body an inch by himself,while the ideal train is carrying him from the station ‘A’ to thestation ‘B’ with considerably high speed. With a very goodapproximation, we can regard the body of the fat guy as an example of a‘macroscopic particle’ whose motion can be described by the Newton'sequation of motion, because his body is regarded only as a ‘particle’when compared with the whole railway system much larger than the body ofthe fat guy.

[1645] In addition, we can regard the weight of the fat guy as a goodexample of the ‘mass’ of the ‘macroscopic particle’. I denote the massby ‘m’. As his weight, say, 91 kilos, (i.e. if I weight 200.8 pounds),‘m’ is described in an equation,

m=91 kilo grams.

[1646] Thus formalized ‘m’ can be used as it is, in the Newton'sequation of motion describing the motion of the fat guy. Let us continuethe discussion using the example. In any situation, the weight of thefat guy is supported by the steel springs used as cushion of the seat ofthe ideal chair of my ideal train. As mentioned before, I sit on theideal chair with perfectly good manner. Therefore, the gravity of theearth exerted on his body balances perfectly with the repelling forceexerted on his body by the steel springs used as cushion of the seat ofthe ideal chair. Therefore, no perpendicular motion of my body is causedunder this balance between these two opposing forces exerted on hisbody. Both of the directions of these two opposing forces, opposite eachother, are, by nature, vertical. Therefore, these two opposing forceshave no relation to any horizontal motion of his body.

[1647] By the way, as described before, the ‘perfectly strait railroadtrack without any bump’ is built on a perfectly horizontal ground.Therefore, either of these two opposing forces have no relations to thetraveling motion of his body along the ‘perfectly strait railroad trackwithout any bump’. Hereafter, as for the forces exerted on his body, weconcentrate, for the time being, only on the forces working along thedirection of the ‘perfectly strait railroad track without any bump’.

[1648] If and when, the ‘ideal train’ is quite violently accelerating,it is impossible for the fat guy to escape from the forward force thatthe ‘ideal train’ exerts on him via the steel springs used as thecushion of the back of the ideal chair (in the case of acceleration).And if and when, the ‘ideal train’ is quite violently decelerating, itis impossible for the fat guy to escape from the backward force that the‘ideal train’ exerts on him via the steel spring used as the seatbelt ofthe ideal chair (in the case of deceleration).

[1649] The fat guy can never escape from either of the forward forcesand/or the backward forth, because he sits on the seat with perfectlygood manner without budging any part of my body an inch by myself,fastened by the seat belt. Let us call the sum of these forces, ‘F’.That is, ‘F’ is the sum of the forward force and the backward force.

[1650] Hereafter, I regard a deceleration as a negative acceleration.Therefore, hereafter, I do not use the word ‘deceleration’, for the sakeof simplicity.

[1651] It should be noted that a mixed team consisting of metallurgicalengineers and mechanical engineers can precisely determine the value of‘F’. In other words, if the members precisely measure the degree ofexpansion and/or the degree of contraction of the steel springs used asthe cushion of the back of the ideal chair, then, they can tell theprecise value of the forward force. And if the members precisely measurethe degree of expansion and/or the degree of contraction of the steelsprings used as the seatbelt of the ideal chair, then, they can tell theprecise value of the backward force. And then, if the value of theforward force and the value of the backward force are summarized, then,the value of ‘F’ is obtained.

[1652] In other words, metaphorically speaking, the value of the forwardforce and the value of the backward force can be precisely measured ifthe steel springs used as the cushion of the back of the ideal chair andthe steel springs used as the seatbelt of the ideal chair are used as aspring scale to measure the value of the force. The value of F, thusobtained can be used as it is in the Newton's equation of motiondescribing the motion of the body of the fat guy.

[1653] It is of grave importance for us to be conscious of that the bodyof the fat guy is never one of the parts of the ideal train, if we wantunderstand the situation in which the fat guy is, on the basis of theNewton's equation of motion. Remember the body of the fat guy is a‘macroscopic particle’. The ideal train exerts the force ‘F’ on this‘macroscopic particle’. What happens? A physicist knows the answer. Theanswer is given by reforming the Newton's equation of motion as,

a=F/m.

[1654] This equation shows that the answer is that the ‘macroscopicparticle’ is accelerated with an acceleration, ‘a’. Remember both thevalue of ‘F’ and the value of ‘m’ has already experimentally measured.Therefore, This means that this equation (i.e. Newton's equation ofmotion, a=F/m) predicts the value of the acceleration of the motion ofthe body of the fat guy, on the basis of experimentally obtained valuesof F and m.

[1655] If and when the velocity of the body of the fat guy is a littleless than the velocity of the ideal train, then, the steel springs usedas cushion of the back of the ideal chair are inevitably pressed by thebody of the fat guy. As a result, the steel springs, in turn, exert therepelling force pushing the body of the fat guy. Under this forwardforce, the body of the fat guy is accelerated according to the Newton'sequation of motion. This makes the velocity of the body of the fat guycatches up with the velocity of the ideal train.

[1656] On the other hand, if and when the velocity of the body of thefat guy is a little greater than the velocity of the ideal train, then,the steel spring used as the seat belt is expanded inevitably bypressure given by the body of the fat guy. As a result, the steelspring, in turn, exerts the repelling force pulling the body of the fatguy. This time, under this backward force, the body of the fat guy isdecelerated, according to the Newton's equation of motion. This makesslow down the velocity of the body of the fat guy back to the velocityof the ideal train.

[1657] Thus, the velocity of with which the body of the fat guy isapproximately equal to the velocity of the ideal train. The train driverof the ideal train can tell the velocity with which the ideal traintravels by using the speed meter of the ideal train. The train driver ofthe ideal train can tell the value of the acceleration of the train bycalculating the rate of change of the velocity of the ideal train.Therefore, the train driver knows the value of the acceleration of thevelocity with which the train travels. Therefore, the train driver knowsthe approximate value of the acceleration of the velocity with whichbody of the fat guy travels. What the Newton's equation of motion saysis that the acceleration of the fat guy predicted by a physicist usingthe Newton's equation coincides with the acceleration of the fat guyapproximately measured by the train driver using the speed meter of theideal train.

[1658] It is clearly the special mechanism of the ideal chair designedon the basis of the nature of steel springs, which keeps the velocity ofthe motion of the body of the fat guy approximately equal to thevelocity of the ideal train. If, for example, the seatbelt fastening thebody of the fat guy did not exist, then, the body of the fat guy wouldbe thrown forward when the ideal train decelerates, because in such asituation, the velocity of his body is kept grater than the velocity ofthe decelerating ideal train.

[1659] The task of the existing seatbelts of a real car is essentiallythe same of that of the seatbelt of the ideal chair of the ideal train.If a car driven by a man were to collide with a tough wall, then, theseat belt decelerates the velocity of the body of the driver in an idealway so as to slow down his velocity back to the velocity of the carbeing suddenly and violently decelerated.

[1660] I will give more strict description of the model of the systemconsisting of the ideal train and the fat guy, below.

[1661] First, I will give the formal and strict way with which the valueof ‘a’ in this example should be experimentally measured. The value of‘a’ is experimentally measured in an absolutely independent way fromthose used in the experimental measurement of the value of ‘m’ and ‘F’.

[1662] Speaking in a concrete way, the value of ‘a’ of the ideal trainis measured using its speed meter; the speed meter includes, say, adynamo directly synchronized to the rotation of a wheel of the idealtrain, and a voltmeter measuring the voltage given by the dynamo. Thetrain driver of the ideal train has only to watch the needle of thevoltmeter to tell the velocity with which the ideal train is traveling.

[1663] To tell explicitly what the Newton's equation of motion tells, wemust make more detailed discussion about the issues of Newton's equationof motion, especially about the issue of the strict definition of the‘acceleration’, ‘a’. Only after this discussion, it will become clearthat if one wants to make a knowledge base system versatile, then, it isrecommended that he should provide a way of knowledge representationwith which the equations of motion including the Newton's equation ofmotion is precisely described.

[1664] Physicists usually regard the ‘straight line’ described in detailthus far, as an example of ‘X’ Cartesian axis. A Cartesian axis is astraight line having an ‘origin’ and having a ‘unit’ of length, withwhich the position of a point is measured.

[1665] The “origin of this ‘X’ Cartesian axis” may be put anywhere onthe ‘X’ Cartesian axis, but once it is put, the origin must be fixed onthe ‘X’ Cartesian axis absolutely, and the origin must not be shiftedafterwards.

[1666] The ‘X’ Cartesian axis itself must stay stationary, and must notbe accelerated or decelerated. The distance (and strictly speaking, thedirection) measured from the ‘origin’ to the macroscopic particle iscalled the ‘X’ coordinate of the particle. The unit of length with whichthe distance is measured may by ‘one foot’ and/or ‘one meter’, etc. Butmost physicists prefer ‘one meter’, as the unit of length. Let us callthe value of the ‘X’ coordinate at which the particle exists the ‘x’.For example, if a macroscopic particle exists 3.0 meters away from theorigin in the positive direction of the ‘X’ Cartesian axis, then, thevalue of x is given by “x=3.0 meters”, if ‘one meter’ is used as theunit of length. A system fixed to such a ‘X’ Cartesian axis is called an‘inertial coordinate system’.

[1667] The ‘perfectly strait railroad track without any bump’ shown inthe previous example is can be regarded as a good example of the “‘X’Cartesian axis” with a very good approximation. Previously described‘straight line’ just means the “‘X’ Cartesian axis”.

[1668] Strictly speaking, the earth is rounding on its own axis and ismaking revolutions around the sun. But usually, the acceleration of thesurface of the earth we feel is negligible when compared with theacceleration of running trains. Therefore, the ‘perfectly straitrailroad track without any bump’ built on a perfectly horizontal andflat ground on the earth is regarded as an ‘inertial coordinate system’,which stays stationary, with an extremely good approximation when a fatguy on a running train is regarded as a macroscopic particle, and theNewton's equation of motion is applied to analyze the traveling motionof the macroscopic particle.

[1669] Let us continue the previous example. We may define the “exactmiddle of the platform of the station ‘A’” as the ‘origin’ of the “‘X’Cartesian axis”. Once we thus define the ‘origin’, our mind about thisdefinition should never be changed thereafter, if we want to think onthe basis of the Newton's equation of motion.

[1670] The ideal train is carrying the body of the fat guy, of course,along the “‘X’ Cartesian axis”. I call the distance (and strictlyspeaking the direction) measured from the ‘origin’ (e.g. the exactmiddle of the platform of the station ‘A’) to the ‘macroscopic particle’(i.e. the center of mass of the body of the fat guy), the “‘X’coordinate” of the macroscopic particle. In the present example, Iregard the direction from the station ‘A’ to the station ‘B’ as thepositive direction. And I regard the direction from the station ‘B’ tothe station ‘B’ as the negative direction. And let me define here avariable ‘x’, which represents the value of the “‘X’ coordinate” atwhich the macroscopic particle exists.

[1671] Continuing above example, if the body of the fat guy is, say,5072.35 meters away from the ‘origin’ (i.e. from the “exact middle ofthe platform of the station ‘A’”), in the positive direction, then thevalue of ‘x’ is 5072.35 meters,

x=5072.35 meters.

[1672] When the particle is traveling along the ‘perfectly straitrailroad track without any bump’, then, the value of ‘x’ of coursechanges as a function of time. Let us call the value of ‘x’ at anabsolute time ‘t’, the ‘x(t)’.

[1673] Continuing above example, as an example, the value of ‘t’ may be,say,

t=am 10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1674] Let us assume that this absolute time is within the time periodbetween the absolute time at which the ideal train started from the ‘A’and the absolute time at which the ideal train arrived at the station‘B’. Let us assume that the macroscopic particle (i.e. the body of thefat guy) is, say, at 5072.35 meters away from the ‘origin of the ‘X’Cartesian axis’ (i.e. from the ‘exact right middle of the platform ofthe station A’) at the absolute time ‘t’ given above, then, ‘x(t)’ is ofcourse described as

x(t)=5072.35 meters,

[1675] where,

t=am 10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1676] Let us think about the next situation at an absolute time after avery small increment of time [let us call this small increment of time,δt] measured from the absolute time, ‘t’,

t=am 10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1677] That is, let us think about the next situation at an absolutetime ‘t+δt’. Let us call the value of ‘x’ at the absolute time ‘t+d t’the ‘x(t+δt)’.

[1678] For example, if the value of δt is, say,

δt=1.00 second,

[1679] then, t+δt is given by

t+δt=am 10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time)+1.00second=am10:55 33.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1680] Continuing this example, if the macroscopic particle is, say, at5089.01 meters away from the ‘origin of the ‘X’ Cartesian axis‘at theabsolute time ‘t+δt’, then, ‘x(t+δt)’ is of course given by

x(t+δt)=5089.01 meters.

[1681] Let us continue above example. Let us call the value of thevelocity ‘v’ of the macroscopic particle at the absolute time ‘t’,‘v(t)’. Physicists knows that the value of ‘v(t)’ at,

t=am10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time)),

[1682] is approximately given by the equation,

v(t)˜x(t+δt)−x(t)}/δt

[1683] ={5089.01 meters−5072.35 meters}/(1.00 seconds)

[1684] =16.66 meters per second

[1685] =59.98 Kilo meters per hour

[1686] =37.25 miles per hour,

[1687] where,

t=am10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1688] and,

δt=1.00 second.

[1689] The value 16.66 meters per second (=37.25 miles per hour) thusobtained is an approximate value of ‘v(t)’ at the absolute time ‘t’. Themajority of the physicists call an approximate equation like thisequation which uses δt whose value is a finite value (i.e. a non-zerovalue), a ‘finite differential equation’ Physicists knows that thestrict definition of v(t) at the absolute time ‘t’ (=am10:55 32.00seconds Jul. the 27, 1999 (Japan Standard Time)), can be obtained in thelimit when we make the value of δt used in the finite differentialequation, approaches infinitively to zero seconds,

v(t)≡Limit(δt→0 second)[{x(t+δt)−x(t)}/δt],

[1690] where

t=am10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1691] In the present invention, the symbol ‘≡’ means that thedefinition of the left hand side of ‘≡’. That is, the definition of theleft hand side of ‘≡’ is given as the right hand side of ‘≡’.

[1692] This equation using the symbol ‘≡’ is described in English by ausual mathematician, that “‘v(t)’ is defined to be the ‘derivative’ of‘x(t)’ at ‘t’”.

[1693] It should be noted that if a man measures experimentally thedistances ‘x(t+δt)’ and ‘x(t)’ and the increment of time, ‘δt’, then, heknows the value of the velocity, ‘v(t)’. For example, if the exact valueof ‘v(t)’ thus obtained is, say, 16.70 meters per second, then, heshould describe the situation by using the equation,

v(t)=16.70 meters/second,

[1694] where

t=am10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1695] In the present case of the motion of the ‘macroscopic particle’(i.e. the fat guy), it is clear that the ‘macroscopic particle’ travelswith reasonably well-defined velocity during the journey. The majorityof the physicists will describe this situation as “the velocity of the‘macroscopic particle’ can be described as a well defined function oftime ‘t’ with reasonable accuracy, if ‘t’ is within the time periodbetween the absolute time at which the ideal train started from thestation ‘A’ and the absolute time at which the ideal train arrived atthe station ‘B’”.

[1696] The majority of mathematicians will describe what I have said, inan idealized way as follows; The value of the “limit of an algebraicexpression”, given by

Limit(δt→0 second)[{x(t+δt)−x(t)}/δt],

[1697] always has a well defined value, as a function of absolute time‘t’, if the ‘t’ is within the period of time. Mathematicians call this“limit of an algebraic expression” the ‘derivative of x’. Usuallymathematicians use a symbol ‘[dx/dt](t)’ to express the ‘derivative ofx’: That is, ‘[dx/dt](t)’ is defined by the equation,

[dx/dt](t)≡limit(δt→0 second)[{x(t+δt)−x(t)}/δt],

[1698] where ‘t’ is an absolute time in the time period between theabsolute time at which the ideal train started from the station ‘A’ andthe absolute time at which the ideal train arrived at the station ‘B’.

[1699] In many cases, most physicists describe [dx/dt](t) simply asdx/dt.

[1700] In a word of mathematics, ‘v(t)’ is defined by the equation,

v(t)≡[dx/dt](t),

[1701] where ‘t’ is an absolute time in the time period between theabsolute time at which the ideal train started from the station ‘A’ andthe absolute time at which the ideal train arrived at the station ‘B’.

[1702] Usually, the majority of the physicists call a strict equationlike this equation, which is defined by infinitely small value of ‘δt’,a ‘differential equation’. According to this definition, the strictdefinition of ‘velocity’ of the ‘macroscopic particle’ is given using adifferential equation, “v(t)≡[dx/dt](t)”.

[1703] Let us continue above example. This time, I will introduceanother finite differential equation, in which v(t) is used as afunction of time. This finite differential equation is used to obtainthe approximate value of the acceleration of the macroscopic particle atan absolute time ‘t’. Before introducing this finite differentialequation, however, I must introduce another finite increment of timewhich is much smaller than the “time consumed by the train to travelfrom station ‘A’ to station ‘B’”, but is substantially longer than theincrement of time, ‘δt’(=1.00 second), which was used previously in afinite differential equation in which x(t) was regarded as a function oftime to give the approximate value of v(t). Let us call the increment oftime used this time by,

t, hereafter.

[1704] For example, I use the value ‘10.00 seconds’ as the value of ‘

t’, because not only ‘10.00 seconds’ is without fail much less than thetime for the ideal train to travel 10000 meters (the distance fromstation ‘A’ to station ‘B’), but also ‘10.00 seconds’ is much longerthan ‘1.00 seconds’ (=δt). Thus it is reasonable to use the value of ‘

t’ given by

t=10.00 seconds.

[1705] As mentioned before, the absolute time ‘t’ at which the finitedifferential equation approximating the value of ‘v(t)’ was,

t=am10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1706] Therefore in the absolute time ‘t+

t’ is given by

t+

t=am10:55 42.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1707] Let us continue the example. If the macroscopic particle (i.e.the fat guy) is traveling with a speed, 16.70 meters per second, at theabsolute time ‘t’, then, v(t) is of course described as

v(t)=16.70 meters per second,

[1708] where,

t=am10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1709] And then, if the macroscopic particle (i.e. the fat guy) istraveling with a speed, say, 16.75 meters per second, at absolute time‘t+

t’, then, ‘v(t+

t)’ is of course described as

v(

t+

t)=16.75 meters per second,

[1710] where,

t+

t=am10:55 42.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1711] The approximate value of the acceleration of the macroscopicparticle at an absolute time ‘t’, i.e. ‘a(t)’ is given by a finitedifferential equation,

a(t)˜{v(

t+

t)−v(t)}/

t,=(16.75 meters per second−16.70 meters per second)/(10.00seconds)=5.00×10⁻³ meters per (second)²,

[1712] where,

t=am10:55 32.00 seconds Jul. the 27, 1999 (Japan Standard Time).

[1713] In the present case of the motion of the macroscopic particle(i.e. the fat guy), the acceleration of the macroscopic particle can bedefined at any absolute time during the journey, because theacceleration is defined on the bases of the velocity, while,‘macroscopic particle’ travels with a reasonably well-defined velocityat any absolute time during the journey. In other words, the finitedifferential equation,

a(t)˜{v(

t+

t)−v(t)}/

t,

[1714] is well defined at any absolute time ‘t’ in the time periodbetween the absolute time at which the ideal train started from thestation ‘A’ and the absolute time at which the ideal train arrived atthe station ‘B’.

[1715] The strict definition of derivative of ‘v(t)’, ‘[dv/dt](t)’, isgiven by using a differential equation as,

[dv/dt](t)≡limit(

t→0 second)[{v(

t+

t)−v(t)}/

t].

[1716] And the strict definition of ‘a(t)’ is given by using thisderivative, in an equation

a(t)≡[dv/dt](t),

[1717] where, ‘t’ is an absolute time in the time period between theabsolute time at which the ideal train started from the station ‘A’ andthe absolute time at which the ideal train arrived at the station ‘B’.

[1718] The last finite differential equation, which was used toapproximate value of ‘a(t)’, can be reformed as another finitedifference equation,

v(

t+

t)˜a(t)·

t+v(t),

[1719] where, ‘t’ is an absolute time in the time period between theabsolute time at which the ideal train started from the station ‘A’ andthe absolute time at which the ideal train arrived at the station ‘B’.

[1720] By inserting the Newton's equation of motion,

F(t)=m·a(t),

[1721] into the finite differential equation, we get another finitedifference equation,

v(

t+

t)˜(F(t)/m)·

t+v(t),

[1722] where ‘t’ is an absolute time in the time period between theabsolute time at which the ideal train started from the station ‘A’ andthe absolute time at which the ideal train arrived at the station ‘B’.

[1723] This finite difference equation explicitly shows what theNewton's equation of motion can be used to predict the value of thefuture velocity of the motion of the macroscopic particle, ‘v(t+

t)’, if the present value of the velocity of the motion of themacroscopic particle, ‘v(t)’, the present value of the force exerted onthe macroscopic particle, F(t), and the mass of the macroscopicparticle, ‘m’ is known at ‘t’. That is, concretely speaking, if the massand the velocity of the macroscopic particle, and the force exerted onthe macroscopic particle at an absolute time ‘t’ are known, then thevelocity of the macroscopic particle at an absolute time ‘t+

t’ is predicted, by using the Newton's equation of motion.

[1724] Remember that the value of ‘F’ can be precisely measured in anexperimental way if ‘steel springs used as cushion in the ideal chair ofthe ideal train’ are uses as spring balances.

[1725] On the other hand the value of ‘m’ can be measured if the fat guyweights himself on a scale, preferably using a ‘simple balance’; Thistime, spring balances are not recommended to be used. The ‘simplebalance’, here, is a balance which has a left arm, a right arm, a panhanging at the end of the left arm, and, another pan hanging at the endof the right arm. When the fat guy weights himself, he must sit down onthe left pan, and appropriate pieces of ounce weights made of, say,lead, must be put on the right pan one after another, until the weightof the fat guy and the weight of the pieces of ounce weights balances.If, say, 3214 pieces of pound weight just balance the fat guy, Then thefat guy finds out that the mass of him is 3214 ounce (=200.8 pound=91Kilo grams). If the mass of the fat guy were measured by spring balance,then, the value of measured weight may include a systematic error;because the centrifugal force exerted on the body of the fat guy causedby the rotation of the earth lessens his apparent weight except just atthe North pole of the earth and/or just at the South pole of the earth.Remember, the centrifugal force exerted on the body of the fat guycaused by the rotation of the earth is never uniform on the surface ofthe earth; it is zero only just at the North pole of the earth and/orjust at the South pole of the earth. And is maximal at anywhere just onthe Equator line of the earth. The ‘mass’ of the fat guy is defined tobe the weight of the fat guy measured by using a simple balance. Themass thus defined includes no such systematic error, because thecentrifugal force caused by the rotation of the earth is exerted notonly on the body of the fat guy sitting on one of the pans of the simplebalance, but also on the pieces of the weight made of lead put on theother pan of the simple balance. As the result the effect of thecentrifugal force is cancelled, and no systematic error is caused. Ingeneral, as a matter of fact, dominants of the physicists prefer tomeasure the mass usually using a simple balance.

[1726] I claim that, the finite differential equation of motionrepresenting approximately the Newton's equation of motion, can bedescribe in a plain English sentence if we use the following twostatements;

[1727] The first statement:

[1728] If

[1729] “the mass of a macroscopic particle is ‘m’, the external forceexerted on it is ‘F’, the position of it is ‘x’, and the velocity of itis ‘v’”, at time=t,

[1730] then,

[1731] “the mass of the macroscopic particle is ‘m’, the position of itis ‘x+vδt’, and the velocity of it is ‘v+(F/m)δt’”, at time=t+δt.

[1732] And,

[1733] the second statement:

[1734] If

[1735] “the mass of the macroscopic particle is ‘m’, the position of itis ‘x+vδt’, and the velocity of it is ‘v+(F/m)δt’”, at time=t+δt,

[1736] then,

[1737] “the mass of a macroscopic particle is ‘m’, the external forceexerted on it is ‘F’, the position of it is ‘x’, and the velocity of itis ‘v’”, at time=t,

[1738] where, ‘δt’ is a sufficiently small increment of time. The valueof this ‘δt’ should be ideally infinitively small, if we want todescribe the Newton's equation of motion exactly.

[1739] However, in many pragmatic computer simulations coded on thebasis of the Newton's equation of motion, such as elementary computersimulations of motion of planets making revolutions around the sun,sufficiently small finite value of ‘δt’, instead of infinitely smallvalue of ‘δt’, is used without any significant problem.

[1740] I claim that, each of these two statements representing theNewton's equation of motion is a kind ‘law of causality’. The term ‘lawof causality’ means the law describing the relationship between thecause and the result. The cause of this rule of causality described byusing the first statement is,

[1741] “the mass of a macroscopic particle is ‘m’, the external forceexerted on it is ‘F’, the position of it is ‘x’, and the velocity of itis ‘v’”,

[1742] and the effect of the rule of causality described by the firststatement is,

[1743] “the mass of the macroscopic particle is ‘m’, the position of itis ‘x+vδt’, and the velocity of it is ‘v+(F/m)δt’”.

[1744] The cause described here determines uniquely the result describedhere, without fail. Newton's equation of motion is a physical lawdescribing the relation between cause and effect that are observedexperimentally, without any exception, in any motion of any macroscopicparticle.

[1745] To explain more concretely, if the state of a macroscopicparticle (i.e. the mass, the position, and the velocity of a macroscopicparticle and the strength and the direction of the external forceexerted on the macroscopic particle) at time=t is explicitly given, thenthe state of the macroscopic particle at time=t+δt is uniquelydetermined. The contrary of this proposition is also true.

[1746] I disclose here a fictional scheme with which the Newton'sphilosophy can be interpreted in an absolutely through manner. At first,let us assume that the world, including the earth, the sun, and theplanets in the universe, can be ultimately decomposed into mutuallyinteracting macroscopic particles. In a word, in this fictional scheme,a planet is regarded as to be composed of, say, grains of sand, whichare regarded as a macroscopic particle. And in the present fictionalscheme, the Newton's equation of motion is regarded as a rule ofcausality describing the time evolution of the system of the macroscopicparticles; All the macroscopic particles moves according to the Newton'sequation of motion. Therefore, the universe can be understood when a mancan know not only the mass, and the initial velocity, of all themacroscopic particles composing the planets but also can know all theforces working between any two macroscopic particles.

[1747] In practice, however, only in special ideal cases in very narrowrange of engineering and/or of science, Newton's equation of motion isof practical use.

[1748] According to my scheme of the ‘Law-of-Causality andModel-of-the-World architecture’, the philosophy of Newton can besummarized in a sentence that “Newton's Model-of-the-World worksaccording to the Newton's Law-of-Causality”.

[1749] Here, the Newton's Model-of-the-World means the macroscopicparticle model of the universe, which I introduced just now. Here, theNewton's Law-of-Causality means the Newton's equation of motion.

[1750] I claim that, “the two statements in a plain English sentencedescribing the finite differential equation of motion representingapproximately the Newton's equation of motion”, can be directlydescribed by using the “sentence pattern of physical and/or mathematicalrules”, which is a data structure for knowledge representation disclosedin the present invention, as,

[1751] _RULE_ Newton's equation of motion _states_if( the mass of amacroscopic particle is m, the external force is F, the position is x,and the velocity is v, at time=t;_)then{the mass of the macroscopicparticle is m, the position is x+vδt, and the velocity is v+(F/m)δt, attime=t+δt;_};_(—)

[1752] The contrary of this proposition of course can be described in asame way, as

[1753] _RULE_ Newton's equation of motion _states_if( the mass of amacroscopic particle is m, the position is x+vδt, and the velocity isv+(F/m)δt, at time=t+δt;_)then{the mass of the macroscopic particle ism, the external force is F, the position is x, and the velocity is v, attime=t;_};_(—)

[1754] Thus, it is clear that Newton's equation of motion can be coveredby the way of knowledge representation of an object-oriented knowledgebase system disclosed in the present invention. I claim here that themodel of the Newton's philosophy constructed according to the‘Law-of-Causality and Model-of-the-World architecture’ can beimplemented as a knowledge base system, if the Newton's equation ofmotion is described as a rule using my “sentence pattern of physicaland/or mathematical rules”, and the values of masses, velocities, andforces are described as a fact.

[1755] It should be noted that “sentence pattern of physical and/ormathematical rules” can be used to explicitly describes that thecontrary of the rule is also true. This shows that the power ofexpression of the way of knowledge representation disclosed in thepresent invention is very rich. Note also that the way of knowledgerepresentation disclosed in the present invention has high readabilityand being easily understood by human who understands elementary Englishgrammar. Of course equivalents of the “sentence pattern of physicaland/or mathematical rules” can easily be implemented in other languagesincluding Japanese, etc. if necessary. However, English is a verypopular language, and as will be shown later, English language is verysuitable to be used in logical inferences introduced originally in thepresent invention. Therefore I selected English in the presentinvention, as the base of the “sentence pattern of physical and/ormathematical rules”.

[1756] Of course, Japanese is also an excellent language both in itsflexibility in the power of expression, and the rich and closeassociation between its words. But the richness of Japanese indescribing an idea in a very many numbers of ways makes Japanese notsuitable to be used as the backbone of an object-oriented knowledge basesystem disclosed in the present invention. As a matter of fact, unlikeEnglishmen, it is not so common to Japanese to consciously describe oneidea in varieties of ways in a their writing. This is because Japanesesentences and words have sufficient richness of association among them,and therefore, paraphrasing in written Japanese, which often only causesconfusion, is not necessary in many cases. Another reason why I choseEnglish rather than Japanese as a best mode to be used as the backboneof an object-oriented knowledge base system disclosed in the presentinvention is that English is a very popular language in the world.

[1757] It should be noted that, symbols for the ‘propositional logic’can be used to formalize a sentence in “sentence pattern of physicaland/or mathematical rules”. For example, the Newton's equation of motiondescribed in the form of “sentence pattern of physical and/ormathematical rules” can be given as,

[1758] _RULE_ Newton's equation of motion _states_if( (The mass of amacroscopic particle is m;_)

(The external force is F;_)

(The position is x;_)

(The velocity is v;_, at time=t;_)then{(The mass of the macroscopicparticle is m;_)

(The position is x+vδt;_)

(The velocity is v+(F/m)δt;_), at time=t+δt;_};_(—)

[1759] The contrary of the proposition of course can be described in asame way. Here, the symbol, ‘

’ means ‘and’. It is recommended that systems of the rules concerningpropositional logic, including the Boolean algebra should be made fulluse of, in knowledge representations disclosed in the present invention.

[1760] However, one thing should be noted. The rule of the‘propositional logic’,

[1761] “If “p→q” is true, then, “

p

q” is true”,

[1762] may be used without any hesitation, where the symbol ‘

’ means ‘not’. However, it is recommended that the rule

[1763] “If “

p

q” is true, then, “p→q” is true”,

[1764] should be used with grate care even though this rule is logicallyconsistent with the other rules of the ‘propositional logic’. I neversay that this rule is no good. I only say that one had better to becareful about the interpretation of this rule.

[1765] For example, I do not think that one can tell that “a proposition‘q’ can be proved mathematically”, only by knowing whether “‘p’ is thetrue or false and ‘q’ is true or false”. Remember the Kurt Gödel's‘incompleteness theorem’, which says that “to be true is one thing, andbeing able to be mathematically proved is another thing”. For details ofthis theorem, see for example {circle over (∘)}“Gehderu no tetsugakuFukanzensei-teiri to Kami no sonzai-ron” and references therein.

[1766] As a matter of fact, if the proposition ‘q’ is a ‘Gödel'sproposition’ of an axiom system, and ‘p’ is another true proposition inthe same axiom system, then, the it is always impossible to prove theproposition ‘q’ on the basis of ‘p’. The ‘Gödel's proposition’ is aproposition which is always true but can not be mathematically provedusing any true proposition included in the same axiom system which isnormal. Gödel showed an explicit universal algorithm by which a ‘Gödel'sproposition’ can be constructed in any mathematical system. Of course,in this case, “

p

q” is always true, because a Gödel's proposition, ‘q’, is always true.This is always the case regardless the choice of the proposition ‘p’from the normal axiom system. But ‘Gödel's proposition’, ‘q’ can neverbe proved on the basis of ‘p’.

[1767] The symbol ‘p→q’ used in the ‘propositional logic’ is exactly a,mathematical formal representation, and it is too simplistic

[1768] always to interpret the ‘p→q’ into an English sentence,“proposition q can be mathematically and/or logically proved usingproposition p”,

[1769] and/or always to interpret ‘p→q’ into an English sentence “If andwhen proposition ‘p’ is satisfied, then proposition ‘q’ is alwayssatisfied afterwards”. It is not guaranteed that such interpretationsalways give us correct conclusions.

[1770] I never say that the rule,

[1771] ““p→q”=“

p

q””

[1772] is no good. Rather I say that this rule is not only logicallyconsistent but also useful. What I say here is that I deliberatelyavoided misuse of it in an object oriented knowledge base systemdisclosed in the present invention.

[1773] Let us return to the main issue. Of course, “data structure forthe description of the five basic sentence patterns of English” can beused to formalize the Newton's equation of motion described using“sentence pattern of physical and/or mathematical rules” in whichsymbols of the propositional logic are used, as,

[1774] _RULE_ Newton's equation of motion _states_if( (S=_ the mass of amacroscopic particle _V=_ is _C=_ m;_)

(S=_ the external force _V=_ is _C=_ F;_)

(S=_the position _V=_ is _C _ x;_)

(S=_ the velocity _V=_ is _C=_ v;_), at time=t;_)then{(S=_ the mass ofthe macroscopic particle _V=_ is _C=m;_)

(S=_ the position _V=_ is _C=_ x+vδt ;_)

(S=_ the velocity _V=_ is _C=_v=v+aδt=v+(F/m)δt;_), at time=t+δt;_};_(—)

[1775] If I use here, sentences in “sentence pattern of definition ofobject”, which is disclosed in the present invention, the followingpropositions are obtained

[1776] _OBJECT_ a macroscopic particle have_VARIABLES mass which_ismeasured by a simple balance.

[1777] _OBJECT_ a macroscopic particle have_VARIABLES Cartesiancoordinate which_is defined as the distance from the origin to themacroscopic particle.

[1778] _OBJECT_ a macroscopic particle have_VARIABLES velocity which_isdefined as the ‘derivative’ of the Cartesian coordinate of themacroscopic particle.

[1779] _OBJECT_ a macroscopic particle have_VARIABLES accelerationwhich_is defined as the ‘derivative’ of the velocity of the macroscopicparticle.

[1780] If I use here, “sentence pattern of function”, which is disclosedin the present invention, the following proposition are obtained:

[1781] _FUNCTION_ ‘differentiation’ _translate_INPUT_ a function of time_into_OUTPUT_ ‘derivative’.

[1782] If I use here, “sentence pattern of ‘ideal thesaurus’”, which isdisclosed in the present invention, the following propositions areobtained

[1783] _NT_ Cartesian coordinate, ‘x’, of a macroscopic particle_is_a_kind_of_BT_ a function of time.

[1784] _NT_ velocity, ‘v’, of a macroscopic particle _is_a_kind_of_BT_ afunction of time.

[1785] A sentence that states that “‘f(t)’ is a mathematical symbol usedto express generally a ‘function of time’”, can be translated into asentence in “sentence pattern of definition of object”, as,

[1786] OBJECT_ ‘a function of time’ have_VARIABLES ‘mathematical symbol’used to express it which_is f(t).

[1787] If I use here, the “sentence pattern of implementation of namesof algorithms-of-processes”, which is disclosed in the presentinvention, the following propositions are obtained;

[1788] _ALGORITHM_ “differentiate f(t)” {f(t)≡limit(δt→0second)[{f(t+δt)·f(t)}/δt];_}

[1789] It is clear that, “sentence pattern of definition of object”,“sentence pattern of ‘ideal thesaurus’”, “sentence pattern ofimplementation of names of algorithms-of-processes”, “sentence patternof physical and/or mathematical rules”, and, “sentence pattern offunction”, are used to describe the idea of the Newton's Mechanics,without almost any awkwardness, fitting exactly along the points of theissue, in a very natural way. This shows the system of forms ofknowledge representations disclosed in the present invention is verysmartly designed.

[1790] If the knowledge is described in sentences in these forms, thealgorithm of association and reasoning given in the present inventionincluding @[algorithm of sentence based object-oriented categoricalsyllogism] @[algorithm of sentence based object-oriented hypotheticalsyllogism], defined later in the present invention can be applied almostautomatically by digital computers. Of course, this form is somewhat tooformal to be read inattentively. But it is extremely easy to ‘translate’this form into more reader friendly one; for example, if only thesymbols, ‘_S═_’, ‘_V═_’, and ‘_C═_’, are removed from a sentence of thisform, then the sentence becomes much readable for a man. This can bedone easily if a computer program for this purpose is used. For example,a commercially available word processors such as the ‘©Word’ presentedby ©Microsoft can be used for this purpose, if its service of replacingparticular pattern into vacancy is used.

[1791] As I mentioned before, English has five basic sentence patterns.It has become evident now that only one of the five basic sentencepatterns of English, (i.e. sentence pattern S+V+C) is necessary todescribe the Newton's equation of motion, if “sentence pattern ofphysical and/or mathematical rules” is used in a way that I havedisclosed in the present invention thus far. Remember that the Newton'sequation of motion is, as I mentioned before, extremely powerful rule todescribe special cases in the material word. Of course, as will be shownlater, the knowledge representations disclosed in the present inventionare so smartly formalized as to be able to use the remaining four basicsentence patterns of English, as well as the sentence pattern S+V+C. Inthe English language, at least about 1500 verbs exist. However, the ‘be’verb is the only verb explicitly used to describe the Newton's equationof motion in the form of “sentence pattern of physical and/ormathematical rules”. Implicitly used verbs are ‘multiply’ (×), ‘divide’(÷), ‘plus’ (+). This means only four of the English verbs are necessaryto describe the Newton's equation of motion.

[1792] As has been shown before in the present invention, the knowledgerepresentations disclosed in the present invention are so smartlyformalized as to be able to give the lexical definition to large numberof the English verbs, as well as the four English verbs describing theNewton's equation of motion. These things will show the extremely mightyrichness of the power of expression of the way of the knowledgerepresentations disclosed in the present invention.

3.3.5. Schrödinger Equation Represented Using a Knowledge RepresentationSystem of an Object-Oriented Knowledge Base Disclosed in the PresentInvention

[1793] It is important to be conscious of the incompleteness and thelimit of the Newton's Equation of motion. A microscopic system, such asa system consists of an electron and an ‘atomic nucleus’ of a hydrogenatom, can not be covered by the Newton's equation of motion. Newton'sequation of motion is the equation of motion of the Newton's Mechanics.An electron is a microscopic particle, and an ‘atomic nucleus’ is also amicroscopic particle. But Newton's Mechanics covers only the systemconsisting only of macroscopic particles. The definition of the word‘microscopic’ will be given later in the present invention. And ingeneral, a molecular system, which is usually microscopic, can not becovered by the Newton's Equation of motion.

[1794] What is called ‘Quantum dynamics’, instead of the ‘Newton'sMechanics’, is necessary to describe correctly a microscopic system.About a hundred years ago, many physicists became conscious of that thebasis of their own physics, i.e. the Newton's equation of motion, isnever almighty. That is, they became conscious that many experimentaldata about microscopic systems can not be precisely described by theNewton's equation of motion. For example, the motion of a travelingelectron can not be precisely described by the Newton's equation ofmotion, because experimental physicists found that an electron dose nottravel straight exactly on a trajectory even when no external force isexerted on the electron. And instead of making a straight trajectory,such an free electron diffracts during its travel, even when no externalforce is exerted on the free electron.

[1795] Physicists in those days were guided by what is called‘correspondence principle’, which tells that the equation of motion thatwill cover the microscopic systems, which is called later ‘Schrödingerequation’, which is an equation of motion of the ‘Quantum dynamics’,should also cover the macroscopic systems, and that when ‘Schrödingerequation’ covers a macroscopic system, what is predicted by ‘Schrödingerequation’ should be exactly consistent with what is predicted by the‘Newton's equation of motion’. In a word, they tried to find an equationof motion that covers not only the motions of microscopic particles butalso covers the motions macroscopic particles.

[1796] The first man who fond the equation of motion was Werner KarlHeisenberg (1901-1976), who proposed what is called the ‘Heisenbergequation of motion’, which covers both microscopic systems andmacroscopic systems in an exact way. ‘Heisenberg equation of motion’ isan equation of motion of the ‘Quantum dynamics’. Soon after, ErwinSchrödinger (1887-1961) proposed what is called ‘Schrödinger equation’.Afterwards, it was found that the ‘Heisenberg equation of motion’ andthe ‘Schrödinger equation’ are exactly equivalent in the mathematicalpoint of view. Nowadays, ‘Schrödinger equation’ is more widely used thanthe ‘Heisenberg equation of motion’.

[1797] Newton's equation of motion is often used even today, becauseNewton's equation of motion is very simple from the point of view ofmathematics, and in many cases, less heavy calculation is necessary tocarry out a computer simulation based on the Newton's equation ofmotion. Schrödinger equation is used when description of microscopicsystems is necessary. Either Schrödinger equation and/or Newton'sequation of motion may be chosen to use case by case. It will be shownthat the Schrödinger equation can be described using the way of theknowledge representation of an object-oriented knowledge base systemdisclosed in the present invention.

[1798] However, Schrödinger equation is not almighty. Both Newton'sequation of motion and Schrödinger equation can cover only motion ofsomething whose velocity is much slower than the velocity of light.

[1799] Afterwards, an equation of motion, which can cover travelingmicroscopic and/or macroscopic particles traveling with any velocity,was presented by P. A. M. Dirac (1902-1984). P. A. M. Dirac combined theEinstein's theory of ‘special relativity’ and the Schrödinger equation.This equation is called the ‘Dirac equation’, which is an equation ofmotion of ‘relativistic quantum dynamics’. It will be shown that the‘Dirac equation’ also be able to be described by using the way of theknowledge base system disclosed in the present invention.

[1800] I show an example in which the way of the knowledgerepresentation of an object-oriented knowledge base system disclosed inthe present invention is used, to describe an equation of motion thatcovers both macroscopic and microscopic material word. It is theSchrödinger equation, which is the one of the most frequently usedequation of motion in the field of research and developments usingquantum dynamics today.

[1801] Motion of electrons in microscopic systems such as atomicsystems, can be precisely described using a Schrödinger equation.However, in general, lower cost of calculation is necessary to solve aNewton's equation of motion, in comparison with the cost of calculationto solve a Schrödinger equation. Therefore, Schrödinger equations areusually used only for systems that can not be covered by the Newton'sequation of motion.

[1802] Before showing how to describe a Schrödinger equation in a formof “sentence pattern of physical and/or mathematical rules”, I will givean outline of the theory of a quantum dynamics.

[1803] The aim for which I give following writing about the outline ofthe background theory of a Schrödinger equation is simply to give thefeeling of ‘having understood’ for the reader of the present invention.I never insist that every proposition in the following writing about theoutline of the background theory of a Schrödinger equation is theabsolute-and-unique ‘truth’. They are only one of possible ways forunderstanding the background of the theory of a Schrödinger equation.But I believe that they are the most suitable to be used to achieve thepresent aim.

[1804] Let us begin. The most fundamental and most puzzling principle ofquantum theory is the ‘uncertainty principle’, which was proposed by afamous physicist, Dr. W. K. Heisenberg in 1926. The ‘uncertaintyprinciple’ is extremely hard to understand by human beings, and has longbeen compelling with ease tremendous number of ambitious young collegestudents in the science course out of the modern physics, before theyrealize even what is ‘uncertain’. Usually the ‘uncertainty principle’ isdescribed purely mathematically. The most elegant mathematicaldescription of the ‘uncertainty principle’ I know is the textbook forcollege student, {circle over (∘)} “Shoto-Ryoshi-Rikigaku” (InJapanese). I recommend that one who has self-confidence in his talent inmathematics should read this textbook if one wants to understand the‘uncertainty principle’ in a mathematical way. But I here describe the‘uncertainty principle’ not only using mathematics but also usingEnglish in a rather naive way.

[1805] One of the attributes of an electron that can never easilyaccepted by any common sense of human being except for physicians isthat “any electron never rests on an infinitively small ‘point’ in aspace how strong the force which bound the electron to the ‘point’ maybe. And instead, any electron always moves around very rapidly in anfinite (i.e. non-zero) expanse of a volume in space”. The majority ofphysicians believe in this proposition about the attributes of anelectron.

[1806] For example many physicists believes in that an electron in anisolated hydrogen atom moves around within an expanse of a volume inspace which can be roughly approximated by an expanse of a volume inspace surrounded by a sphere whose radius is approximately 0.529×10⁻¹⁰meters. Physicists usually call this length of 0.529×10⁻¹⁰ meters the‘Bohr radius’, which is usually denoted by ‘a_(B)’.

a _(B)=0.529×10⁻¹⁰ meters

[1807] Here ‘an isolated hydrogen atom’ means a hydrogen atom in vacuumnot in a form a component of a molecule but in a form of atom isolatedfrom any other atoms. In many cases, by the way, many metallurgistsoften regards the ‘Bohr radius’ and/or other values very near to the‘Bohr radius’ as the ‘radius’ of ‘an isolated hydrogen atom’, when theyuse their pragmatic ‘hard sphere model’ of an isolated hydrogen atom.

[1808] Physicists believe in that what is called an ‘atomic nucleus’always exists in any atom. The ‘atomic nucleus’ of an isolated hydrogenatom exists, of course, at the center of the previously describedsphere. In a usual environment on the earth in which human beings live,the ‘radius’ of the atomic nucleus of an isolated hydrogen atom is about10⁻¹⁵ meters, which is about {fraction (1/10000000)} of the ‘Bohrradius’. In this sense, a ‘nuclear size’ is usually much smaller than an‘atomic size’.

[1809] When an electron in an isolated hydrogen atom is makingrevolutions in an orbit around the atomic nucleus of the isolatedhydrogen atom stationary, then the ‘electrostatic-attractive-forceworking between the electron and the atomic nucleus’ and the‘centrifugal force exerted on the electron’ balance each other. Thisbalance of two types of forces prevents the electron in an isolatedhydrogen atom from absolutely sucked and swallowed by the atomic nucleusof the isolated hydrogen atom. In other words, this balance is a reasonwhy an isolated hydrogen atom dose not easily crushes and compressedinto a small lump whose size is about as large as nuclear size. Ofcourse I agree this proposition.

[1810] Common misconception is that only the previously mentionedbalance between electrostatic-attractive-force and the centrifugal forceis the mechanism that prevents an isolated hydrogen atom from easilycrushing and being compressed into a small lump of nuclear size.

[1811] The fact is that even when the electron in an isolated hydrogenatom is not making revolutions around the atomic nucleus of the isolatedhydrogen atom, the isolated hydrogen atom dose not crush-and-compressedinto a small lump of nuclear size.

[1812] Many physicists may be paradoxically confused here about what isthe meaning of the word ‘making revolutions’ used here. In order toprevent this, I give a mathematical definition here readable only forphysicists. When I say that “An electron in an isolated hydrogen atom isnot making revolutions around the atomic nucleus of the isolatedhydrogen atom”, I mean that “The angular momentum of the electron,[h/(2π)]l(l+1), is zero”, where h is the Planck's constant, and ‘l’ isthe ‘azimuthal quantum number’”. In a word, when I say that “an electronin an isolated hydrogen atom is not making revolutions around the atomicnucleus of the isolated hydrogen atom”, I mean that “the ‘azimuthalquantum number’ of the electron in a isolated hydrogen is zero, (i.e.l=0)”. And when I say that “an electron in an isolated hydrogen atom ismaking revolutions around the atomic nucleus of the isolated hydrogenatom”, I mean that “the ‘azimuthal quantum number’ of the electron in aisolated hydrogen is non zero, (i.e. l≠0)”.

[1813] Remember here the previously mentioned attributes of an electron,that “any electron never rests on an infinitively small ‘point’ in aspace how strong the force which bound the electron to the ‘point’ maybe, and instead, any electron always moves around very rapidly in anexpanse of a volume in space”. Therefore, an electron in an isolatedhydrogen atom keeps moving around very rapidly in the atom even when theelectron is not making revolutions around the atomic nucleus of theisolated hydrogen atom, even while the electron is strongly attracted bythe atomic nucleus via the electrostatic force.

[1814] The first question may arise here from ones who know the Coulomblaw. That is, as the electrostatic-attractive-force is very mighty whenthe electron approaches very closely to the surface of the atomicnucleus of the isolated hydrogen atom because the nuclear size is verysmall. Why dose the electron not absorbed by the atomic nucleus of theisolated hydrogen atom by this mighty electrostatic-attractive-force?

[1815] The second question may arise here that how the electron keepsmoves around very rapidly in the atom without making revolutions aroundthe atomic nucleus of the isolated hydrogen atom?

[1816] The answer to the first question is as follows. Theelectrostatic-attractive-force is of course working between the electronand the atomic nucleus even when the electron in an isolated hydrogenatom is not making revolutions around the atomic nucleus of the isolatedhydrogen atom. In addition, this electrostatic-attractive-force isjudged to be ‘very mighty’ when one considers that the mass of anelectron (9.11×10⁻³¹ kg) is extremely small and that the minimumdistance between the electron and the atomic nucleus (10⁻¹⁵meters˜({fraction (1/100000)})×a_(B)) is extremely short. Remember here,however, the attribute of an electron here that “any electron neverrests on an infinitively small ‘point’ in a space however strong theforce which bound the electron to the ‘point’ may be, and instead, anyelectron always moves around very rapidly in an expanse of a volume inspace”. Here, the atomic nucleus of the hydrogen atom is regarded as the‘point’, and the electrostatic force between the electron and the atomicnucleus is regarded as the “force that bound the electron to the‘point’”.

[1817] Of course, the stronger the force which bounds the electron tothe ‘point’ is, the narrower the expanse of a volume in space around thepoint in which the electron moves around rapidly is. However, asdetailed quantum mechanical calculations carried out on the basis of‘Schrödinger equation’ shows (see any textbook of quantum dynamics, forexample, {circle over (∘)}“Shoto-Ryoshi-Rikigaku” (In Japanese) or{circle over (∘)}“Problems in Quantum Mechanics, third edition” (InEnglish)), the electrostatic-attractive-force is not strong enough toconfine the electron in the atomic nucleus. Instead, theelectrostatic-attractive-force confines the electron in an expanse of avolume in space which is approximated by a volume in space surrounded bya sphere, whose radius is about 0.529×10⁻⁸ meters (=a_(B)), and at thecenter of which the atomic nucleus of the isolated hydrogen atom exists.

[1818] Some physicists may be confused here again. I give here adetailed discussion on the basis of the mathematically described‘uncertainty principle’. Is it really true that the stronger the forcewhich bound the electron to the ‘point’ becomes, the narrower theexpanse of a volume in space around the point in which the electronmoves around rapidly? Only for them, I use here mathematical equation.The ‘uncertainty principle’ in 3-dimensional space can be described byan equation,

(3^(1/2)·ΔR)·(3^(1/2)·Δp)˜h,

[1819] where ΔR is the uncertainty of the position of the electron, andΔp is the uncertainty of the momentum of the electron, 3^(1/2) is thesquare root of 3, and h is the Planck constant,

h=6.63×10⁻³⁴ Joule·second,

[1820] here, ‘Joule’ is the unit of energy. 1 Calorie equals to 4.2Joule. Momentum ‘p’ of an electron is a quantity that a physicist oftenuses, and is defined by an equation,

p≡m×v Kg·meter/second,

[1821] where m is the mass of the electron, (m=9.11×10⁻³¹ Kg), and v isthe velocity with which the electron travels.

[1822] A physicist knows that if and when an electron is movingstationary around a central point under any static attractive forcebetween the electron and the central point, then that the kinetic energyof the moving electron balances with the potential energy of theelectron, V(R), which is defined on the basis of the static attractiveforce, as a function of the distance ‘R’ between the electron and thecentral point.

[1/(2 m)]p²˜V(R),

[1823] where, m is the mass of an electron,

m=9.11×10⁻³¹ Kg.

[1824] In the case of an electron in an isolated hydrogen atom, thestatic attractive force is the electrostatic force working between theelectron and the atomic nucleus, and, V(R), is given by the equation,called the ‘Coulomb's Law’,

V(R)=[1/(4πε₀)][e ² /R]˜[1/(4πε₀)][e ² /ΔR],

[1825] where, ‘ε₀’ and ‘e’ are constants which a physicist often usesand are given by

ε₀=8.85×10⁻¹² Farad meter,

e=1.60×10-19 Coulomb.

[1826] Equation, [1/(2 m)]p²˜V(R), which is widely accepted by dominantof physicists, shows that the stronger the force which bound theelectron to the ‘point’ is, the greater the absolute value of ‘p’becomes. As the electron is confined in a finite expanse of a volume inspace, the electron should be zigzagging. Therefore, the uncertainty ofthe value of ‘p’ should be approximately equal to the absolute value of‘p’. Therefore, in a rough approximation, following two equations areused,

Δp˜p,

[1827] Therefore, it is concluded that the stronger the force whichbound the electron to the ‘point’ is, the greater the absolute value of‘Δp’ becomes. If and when the value of ‘Δp’ becomes greater, the valueof ‘ΔR’ becomes inevitably small; remember the equation “Δp·ΔR=h”, where‘h’ is a constant. Therefore, in a word, the greater the force whichbound the electron to the ‘point’ is, the smaller the value of ‘ΔR’becomes. If the value of ‘ΔR’ is smaller, then the width of the expanseof a volume in space around the point in which the electron moves aroundrapidly becomes narrower, because ‘ΔR’ is just equal to the width.

[1828] Furthermore, if previously described there equations

(3^(1/2)·ΔR)·(3^(1/2)Δp)˜h,

[1/(2 m)]p²˜[1/(4πε₀)][e²/ΔR],

Δp˜p,

[1829] are used, one can obtain,

ΔR˜(2πε₀ h ²)/(9 me ²)

=1.16×10⁻¹⁰ m

=1.1×(2×0.529×10⁻¹⁰ m)

=1.1×(2×a _(B)).

[1830] This result shows that the value of ‘ΔR’ obtained using theequation representing the ‘uncertainty principle’ is judged, withreasonably good precision (the error is only about 10%), to be equal tothe value of the ‘Bohr radius’. Thus, the ‘Bohr radius’ can be regardedwith good approximation as the value representing the typical width ofthe expanse of a volume in space in which the electron of an isolatedhydrogen atom always moves around very rapidly. These discussions givenabove are the detailed discussion on the basis of the mathematicallydescribed ‘uncertainty principle’.

[1831] To give the answer to the second question, it is important toclearly recognize that

[1832] “that an electron is making revolutions around the atomic nucleusof the isolated hydrogen atom”, which I called the ‘revolution of anelectron’ as one thing,

[1833] and,

[1834] “that an electron in an isolated hydrogen atom keeps movingaround very rapidly in the atom even when the electron is not makingrevolutions around the atomic nucleus of the isolated hydrogen atom”,which many physicists often call a ‘Zitterbewegung’, as a quite anotherthing.

[1835] Revolution of an electron can be easily ‘understood’ not only byphysicists but also by people other than physicists, because most of thepeople knows the thesis of Galileo, which tells that the earth makerevolutions around the sun. They understand the concept of ‘revolutionof an electron around the atomic nucleus’ by analogy with the thesis ofGalileo of ‘revolution of the earth around the sun’.

[1836] On the other hand, many physicists believes in that‘Zitterbewegung’ can not be understood by any analogy with eventsobserved by human eyes in the daily life of people.

[1837] When the electron is not making revolutions around the atomicnucleus of the isolated hydrogen atom, no centrifugal force is exertedon the electron. Therefore, if the Newton's equation of motion wereexactly valid in this microscopic system (i.e. in this system of theisolated hydrogen atom), the electron is attracted directly making alinear trajectory into the atomic nucleus of the isolated hydrogen atom,and collides with the atomic nucleus of the isolated hydrogen atom. Dosean electron really travels in such a way? As far as I know, the answeris “nobody knows”, because it has not been reported that anyone has eversucceeded in observing such a ‘trajectory’ of ‘Zitterbewegung’ of anelectron, in an experiment repeatable by anyone at anywhere and anytime.

[1838] Instead, the majority of the physicists believe in that it is inprinciple impossible to observe experimentally the exact trajectory ofsuch a motion (‘Zitterbewegung’) of an electron. In other words, it isimpossible to trace both the exact place and the exact velocity of anelectron at the same time. This principle is what is called the‘uncertainty principle’ proposed by Heisenberg. That is, if theuncertainty in the momentum of an electron is ‘Δp’ and the uncertaintyin the place of an electron is ‘ΔR’, then, the equation,

(3^(1/2)·ΔR)·(3^(1/2)·Δp)˜h,

[1839] is satisfied at any time at any place. Therefore, both the valueof ‘ΔR’ and the value of ‘Δp’ can not approach infinitely to zero at thesame time.

[1840] To avoid confusion, I clearly say here that many of thephysicists believe in that if and when the electron is not makingrevolutions around the atomic nucleus of the isolated hydrogen atom thatis not energetically excited, then the ‘Zitterbewegung’ is the only‘motion’ the electron makes. And the electron dose not make any othermotion. And, many physicists believe in that if and when an electron inan isolated hydrogen atom is making revolutions in an orbit around theatomic nucleus of the isolated hydrogen atom stationary, not onlyrevolution but also ‘Zitterbewegung’ is the ‘motion’ of the electron,because the ‘uncertainty principle’ dose not break down even when anelectron is making revolutions.

[1841] When the state of an electron in an isolated hydrogen atom is inthe energetically most stable state (i.e. in the state with minimalkinetic energy), then, the electron is not making revolutions around theatomic nucleus. This is, an electron making revolutions has excessenergy than an electron not making revolutions. To say more detail, anelectron making revolutions has the kinetic energy of the travelingmotion in an orbit as well as the kinetic energy of ‘Zitterbewegung’. Onthe other hand, an electron that is not making revolutions has only thekinetic energy of ‘Zitterbewegung’.

[1842] According to the Maxwell's theory of ‘electromagnetism’, anycharged particle making revolutions emits radiation, which is called the‘synchrotron radiation’. The excess energy (i.e. the kinetic energy ofthe revolutionary motion in an orbit) is spontaneously emitted in thecourse of time, as the photo energy of the ‘synchrotron radiation’ untilthe electron making revolutions stops making revolutions. And finallyand inevitably the hydrogen atom settles down into a state in which theelectron dose not make revolutions any more. The frequency of theelectromagnetic wave radiated from the isolated hydrogen atom as the‘synchrotron radiation’ is about equal to the frequency of therevolutions made by the electron traveling around the atomic nucleus ofthe isolated hydrogen atom.

[1843] Many of the experimental molecular physicists can measure thefrequency of the electromagnetic wave mentioned above using what iscalled the ‘spectrometer’ and what is called the ‘photon counter’. Mostof the molecular physicists know that the precision of the measurementof the frequency is tremendously high, when they measure ‘synchrotronradiations’ made by many hydrogen atoms that synchronizes with eachother by stimulating each other via their radiations; This phenomena isknown as ‘Light Amplification by Stimulated Emission of Radiation’,usually called the laser. In this sense, a man can measure the frequencyof such revolutions made by the electron traveling around the atomicnucleus of an isolated hydrogen atom, with great precision.

[1844] Of course, no one has observed the precise trajectory of such arevolution of an electron, because it is almost certainly impossible toobserve the ‘Zitterbewegung’ always accompanying the revolution of anelectron. An electron in an isolated hydrogen atom usually dose notmaking revolutions around the atomic nucleus unless the isolatedhydrogen atom is energetically excited by the world outside of theisolated hydrogen atom by means of irradiation, particle impact, etc. Ina word, in most of the usual isolated hydrogen atoms, the electron isnot making revolutions around the atomic nucleus of its hydrogen atom.

[1845] The majority of physicists believe in that “Only ‘theprobabilistic distribution of existence of an electron in the expanse ofa volume in space in which an electron is moving around very rapidly(i.e. an electron is making the ‘Zitterbewegung’)’ can be observed byhuman beings”. And they believe in that “The precise trajectory of the‘Zitterbewegung’ can not be observed by human beings”. Do not beconfused here. Strictly speaking, the word ‘revolution of electron’ usedin the sentence, “the revolution of an electron in an isolated hydrogenatom around the atomic nucleus of the isolated hydrogen atom”, means‘the revolution of ‘the probabilistic distribution of existence of anelectron and/or its phase in the expanse of a volume in space in whichan electron is making the ‘Zitterbewegung’’’. The experimentalmeasurement of the ‘frequency of the revolution of an electron in anisolated hydrogen atom’ is not equal to the measurement of thetrajectory of the ‘Zitterbewegung’.

[1846] Only a small number of the physicists believes in the usefulnessof tagging along the detailed discussion of the exact trajectory of‘Zitterbewegung’. But they continue hot and open discussions about theissue of ‘Zitterbewegung’. Shin'ichiro Tomonaga, who is a famousphysicists and is a Nobel prize winner, had great interest in the‘theory of measurement’ all along the works in his study, but to manyphysicist's sorry, he died of cancer before he had completed the plan ofpublishing a textbook to be named ‘Ryoshi rikigaku III’, in whichdetailed and careful discussion about the issue of ‘theory ofmeasurement’ would be given. But a part of his work about this issue isdescribed in his {circle over (∘)}“Supin ha Meguru”(“Spin the Spin”).

[1847] If and when an isolated electron, for example, exists in vacuumunder a spatially uniform external electric field, the electron is ofcourse accelerated and begins to travel in the direction of the externalelectric field. More strictly speaking, ‘the probabilistic distributionof existence and/or its phase of an isolated electron in the expanse ofa volume in space in which an electron is making the ‘Zitterbewegung’’is accelerated and begins to travel, if and when the isolated electronexists in vacuum under a spatially uniform external electric field. Theelectron of course keeps traveling even after the spatially uniformexternal electric field ceases to exist. In other words, the electron ismaking a translation motion as well as ‘Zitterbewegung’.

[1848] The majority of the physicists believe in that if and when the‘probabilistic distribution of existence of an electron in the expanseof the volume in space’ travels as a bunch with velocity v, then, thebunch acts like a ‘packet’ of ‘wave’; that is, the probabilisticamplitude of existence of an electron raises and falls periodicallywithin the traveling packet along a line which defines the center of thetrack of the traveling motion. The wave length of this wave is calledthe ‘de Broglie wavelength’, which I denote here by the symbol, ‘λ’. Thevalue of ‘λ’ can be roughly estimated by the equation which was given byde Broglie,

λ˜h/(mv),

[1849] where h is the Planck's constant, m is the mass of the electron,and v is the velocity of the translation motion of the travelingelectron.

[1850] Many physicists usually call this wave the “matter wave” of theelectron. A “matter wave” of the electron diffracts. Erwin Schrödinger(1887˜1961), who is a well known physicist, found out that the spatialdistribution and the time evolution as well as the phase of the ‘matterwave’ of an electron can be precisely described as a mathematicalfunction of the absolute time ‘t’ and of the Cartesian coordinates(x,y,z). Physicists usually call this mathematical function the ‘wavefunction’ of the electron. And they usually express this ‘wave function’by the symbol ‘ψ(x,y,z,t)’.

[1851] If I describe this discussion in a more mathematical style, then,I can say in the following manner;

[1852] The value of the square of the absolute value of ‘ψ(x,y,z,t)’,i.e.

|ψ(x,y,z,t)|·|ψ(x,y,z,t)|,

[1853] equals strictly to the probabilistic density of existence of anelectron around the point, having the Cartesian coordinate, ‘(x,y,z)’,at the absolute time, ‘t’. In a simplified but somewhat confusingdescriptions, the ‘(x,y,z)’ is called the ‘Cartesian coordinate of theelectron’. Most of the physicists use this simplified description, butusually they do not believe in that “the ‘Cartesian coordinate of theelectron’ of an electron, ‘(x,y,z)’, can be experimentally determined asa function of time”. This ‘function of time’, if it would exist,describes the trajectory of the ‘Zitterbewegung’ of an electron. In aword, dominant of the physicists dose not know that such a ‘function oftime’ really exists or not, even while they use a simplified description‘Cartesian coordinate of the electron’. What I want to say here is thatwhether such a ‘function of time’ exists or not dose not have anyrelation to the claims disclosed in the present invention.

[1854] Erwin Schrödinger also showed the guide line along which theimplementation of the wave function ‘ψ(x,y,z,t)’ can be obtained. Thatis, he showed that if a man solves the mathematical differentialequation, which is usually called the ‘Schrödinger equation’ for theelectron, strictly, he gets the implementation of the wave function ofthe electron, ‘ψ(x,y,z,t)’. Details of the background theory of theSchrödinger equation is given in {circle over(∘)}“Ryoshi-Rikigaku”(“Quantum Mechanics”), and in {circle over(∘)}“Supin ha Meguru”(“Spin the Spin”).

[1855] The dominant of the physicists knows that the Schrödingerequation for an electron in an isolated hydrogen atom is given by;

Hψ(x,y,z,t)=[ih/(2π)](∂/∂t)ψ(x,y,z,t),

[1856] where ‘i’ is what mathematicians calls an ‘imaginary number’,which is defined by

i=(−1)^(1/2),

[1857] and, ‘(x,y,z)’ is the Cartesian coordinate of the electron, and,‘H’ is a mathematical operator usually called an ‘Hamiltonian’. ‘H’ isdefined by

H=−[1/(2 m)]·[h ²/(2π)² ]·Δ+V(x,y,z,X,Y,Z),

[1858] where ‘m’ is ‘the mass of an electron’, ‘(X,Y,Z)’ is theCartesian coordinate of the atomic nucleus, ‘h’ is the Plank's constant,‘Δ’ is a kind of mathematical operator, which mathematicians call aLaplacian, defined by,

Δ≡{∂²/(∂x)²}+{∂²/(∂y)²}+{∂²/(∂z)²},

[1859] where, and, ‘{∂²/(∂x)²}’ means a mathematical operation in whichderivative by ‘x’ is carried out twice, ‘{∂²/(∂y)²}’ means amathematical operation in which derivative by ‘y’ is carried out twice,and ‘{∂²/(∂z)²}’ means a mathematical operation in which derivative by‘z’ is carried out twice. And ‘V(x,y,z,X,Y,Z)’ is the electrostaticpotential energy of the charged particles system consist of the electronand the atomic nucleus of the hydrogen atom. According to the Maxwell'stheory of electromagnetism, this V(x,y,z,X,Y,Z) is given by

V(x,y,z,X,Y,Z)=[e ²/(4πε₀)]·(1/R ²),

[1860] where, R is the distance between the electron and the atomicnucleus, which is given by the equation,

R=[(x-X)²+(y-Y)²+(z-Z)²]^(1/2),

[1861] sand ‘e’ is the a constant what physicists call the ‘elementaryelectron charge’,

e=1.60×10⁻¹⁹ Coulomb,

[1862] and ‘≢₀’ is another constant what physicists call the‘permittivity of vacuum’

ε₀=8.85×10⁻¹² Farad·meter⁻¹.

[1863] Usually, ‘(X,Y,Z)’ is set as

(X,Y,Z)=(0,0,0),

[1864] for the sake of simplicity, without losing any generality of thepresent discussion.

[1865] A mathematicians can prove mathematically that a solution to thisSchrödinger equation exists, and a physicists know how to get themathematically strict solution of this Schrödinger equation to give theimplementation of ‘ψ(x,y,z,t)’.

[1866] Strictly speaking, Hamiltonian for an electron in an isolatedhydrogen atom is not perfectly strict. And it must be slightly adjustedby introducing the mass of the atomic nucleus of the isolated hydrogenatom, which is usually denoted by M. This slight adjustment is extremelyeasy for a physicist, but I do not make this slight adjustment for thesake of simplicity. A physicist knows the concept of ‘reduced mass’ anddo this slight adjustment within thirty seconds.

[1867] Let us discuss here a special situation in which

V(x,y,z)=0,

[1868] is used as the ‘V(x,y,z)’ in the Schrödinger equation. ThisSchrödinger equation describes an electron traveling in vacuum free ofany external force or any external field (i.e. a free electron). In thiscase, the Schrödinger equation is given by,

H _(FψF)(x,y,z,t)=[ih/(2π)](∂/∂t) ψ_(F)(x,y,z,t),

[1869] where ‘H_(F)’ is given by

H _(F)=−[1/(2 m)]·[h ²/(2π)²··Δ.

[1870] A solution of this Schrödinger equation represents the ‘matterwave’ of the free electron:

ψ_(F)(x,y,z,t)=A·exp[(i/λ)(x−vt)],

[1871] where, A is a constant, and ‘A’ is given by

λ=h/(4πm),

[1872] where the last equation gives the exact ‘de Broglie wavelength’of the ‘matter wave’, ‘ψ_(F)(x,y,z,t)’ of the free electron.

[1873] According to the quantum dynamics, the ‘v’ means the velocity ofthe center of mass of the ‘matter wave’. In most cases, the motion ofthe ‘center of mass’ of a ‘matter wave’ can be described using theNewton's equation of motion, unless the electron is traveling withextremely high speed. About the details of this issue, see for example,{circle over (∘)}“Shoto-Ryoshi-Rikigaku”, and “Quantum Mechanics”.

[1874] Thus far, only the one-electron systems are discussed, but it isusually very easy to get a Schrödinger equation describing a system thatis composed of more than one electrons. In general, to solve theSchrödinger equation of a system that comprises many electrons whit highprecision using, say, configuration integration method, is usuallydifficult. But to solve such a Schrödinger equation approximately withlow precision using, say, the object-oriented Hückel method, for examplenot very difficult.

[1875] As an example of n-electron system, we can discuss here, theelectron system in a molecular system composed more than two atoms. Ifthe wave function of the electron system is obtained, not only the mostof the chemical properties of the molecule can be predicted, but alsothe modes of the molecular vibrations of the molecules can be described.

[1876] Let us call the number of electrons in this molecular system ‘n’,and let us call the number of atoms constituting the molecular system,‘N’. Usually, the Schrödinger equation for the electrons in a molecularsystem can be obtained easily. It can be obtained only by slightlymodifying the previously mentioned ‘one-electron’ Schrödinger equationof an electron in an isolated hydrogen atom. Here I only roughly discussthis issue; The Schrödinger equation of n-electron system in themolecule is given by,

H _(n)ψ_(n)(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . x _(n) ,y _(n) ,z _(n),t)=[ih/(2π)](∂/∂t) ψ_(n)(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . x _(n) ,y_(n) ,z _(n) ,t),

[1877] where (x_(j),y_(j),z_(j)) is the Cartesian coordinate of the j'thelectron in the n-electron system, and ‘t’ is the absolute time. ‘H_(n)’not only includes the (x_(j),y_(j),z_(j))'s, but also includes theCartesian coordinates of the atomic nucleuses of the atoms whichcomposes the molecule; i.e. ‘H_(n)’ includes the Cartesian coordinatesof the atomic nucleuses, the (X_(I),Y_(I),Z_(I))'s,

(X _(I) ,Y _(I) ,Z _(I)) (I=1, 2, . . . , N),

[1878] where, ‘N’ is the number of the atoms constituting the molecule.

[1879] Of course, the mass of an electron, the ‘elementary electroncharge’, and the ‘permittivity of vacuum’ are also included in ‘H_(n)’.

[1880] It is not difficult for a physicists to write down suchSchrödinger equations for many body systems; ‘H_(n)’ is given by,

H _(n)=−[1/(2 m)]·[h ²/(2π)²]·[Δ₁+Δ₂+ . . . +Δ_(n) ]+V _(n)(x ₁ ,y ₁ ,z₁ ,x ₂ ,y ₂ ,z ₂ , . . . ,x _(n),y_(n),z_(n) ,X ₁ ,Y ₁ ,Z ₁,X₂ ,Y ₂ ,Z ₂, . . . ,X _(N) ,Y _(N) ,Z _(N)),

[1881] where, Δ_(j) is the Laplacian defined for electron ‘j’, as

Δ_(j)≡{∂²/(∂x _(j))²}+{∂²/(∂y _(j))²}+{∂²/(∂z _(j))²},

[1882] and,

[1883] V_(n)(x₁,y₁,z₁,x₂,y₂,z₂, . . .,x_(n),y_(n),z_(n),X₁,Y₁,Z₁,X₂,Y₂,Z₂, . . . ,X_(N),Y_(N),Z_(N)), is theelectrostatic potential energy of the charged particles system includesthe electrons and the atomic nucleuses of the molecule. This is given by$\begin{matrix}{{V_{n}( {x_{1},y_{1},z_{1},x_{2},y_{2},z_{2},\ldots \quad,x_{n},y_{n},z_{n},X_{1},Y_{1},X_{2},Y_{2},Z_{2},\quad \ldots \quad,X_{N},Y_{N},Z_{N}} )} =} \\{\lbrack {^{2}/( {4\pi \quad ɛ_{0}} )} \rbrack \cdot ( {{A_{1}/R_{1,1}} + {A_{1}/R_{2,1}} + \quad \ldots \quad + {A_{1}/R_{n,1}} +} } \\{{A_{2}/R_{1,2}} + {A_{2}/R_{2,2}} + \quad \ldots + {A_{2}/R_{n,2}} +} \\{\ldots +} \\{ {{A_{N}/R_{1,N}} + {A_{N}R_{2,N}} + \quad \ldots \quad + {A_{N}/R_{n,N}}} ) +} \\{\lbrack {^{2}/( {4\quad \pi \quad {ɛ\quad}_{0}} )} \rbrack \cdot ( {{1/r_{1,2}} + {1/r_{1,3}} + {1/r_{1,4}} + {1/r_{1,5}} + \quad \ldots \quad + {1/r_{1,n}} +} } \\{{1/r_{2,3}} + {1/r_{2,4}} + {1/r_{2,5}} + \quad \ldots \quad + {1/r_{2,n}} +} \\{{1/r_{3,4}} + {1/r_{3,5}} + \quad \ldots + {1/r_{3,n}} +} \\{{1/r_{4,5}} + \quad \ldots \quad + {1/r_{4,n}} +} \\{\ldots +} \\{ {1/r_{n,n}} ),}\end{matrix}$

[1884] where, ‘A_(I)’ is the atomic number of the I'th atom in themolecule, and ‘R_(i,I)’ is the distance between the I'th atom and thei'th electron in the molecule, which is defined as

R _(i,I)≡[(x _(i) −X _(I))²+(y _(i) −Y _(I))²+(z _(i) −Z _(I))²]^(1/2),

[1885] and ‘r_(j,k)’ is the distance between the j'th electron and thek'th electron in the molecule, which is defined as

i r_(j,k)≡[(x _(j) −x _(k))²+(y _(j) −y _(k))²+(z _(j) −z _(k))²]

[1886]1/2.

[1887] The Schrödinger equation,

H _(n)ψ_(n)(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . ,x _(n) ,y _(n) ,z _(n),t)=[ih/(2π)](∂/∂t)ψ_(n)(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . ,x _(n) ,y_(n) ,z _(n) ,t),

[1888] describes the molecular system.

[1889] Usually, a macroscopic particle is consists of microscopicparticles, (i.e. consists of atoms and/or of molecules). Therefore, themotion of most of the macroscopic particles can be described using thisSchrödinger equation and a Schrödinger equation for motion of atomicnucleuses. The description of motion of macroscopic particles on thebasis of such Schrödinger equations is consistent with the descriptionof motion of macroscopic particles on the basis of the Newton's equationof motion, because a Schrödinger equation is contrived so as to the‘correspondence principle’ is satisfied.

[1890] The Schrödinger equation is a differential equation from thepoint of view of mathematics. If the way to approximate a differentialequation by using a finite differential equation, which has beendescribed in detail previously in the present invention, is used, then,a finite differential equation approximation the Schrödinger equation,

H _(n)ψ(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . ,x _(n) ,y _(n) ,z _(n),t)=[ih/(2π)][ψ_(n)(x ₁ ,y ₁ ,z ₁ , . . . ,x _(n) ,y _(n) ,z _(n) ,t+δt)−ψ_(n)(x ₁ ,y ₁ ,z ₁ , . . . x _(n) ,y _(n) ,z _(n) ,t)]/δt.

[1891] is obtained, where δt is a value of the increment of time whichis finite but small enough to make this finite differential equation angood approximation of the Schrödinger equation. This finite differentialequation is readily transformed into,

ψ_(n)(x ₁ ,y ₁ ,z ₁ , . . . ,x _(n) ,y _(n) ,z _(n) ,t+δt)=ψ_(n)(z ₁ ,y₁ ,z ₁ ,t)+[2π/(ih)]H _(n)ψ_(n)(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . ,x_(n) ,y _(n) ,z _(n) ,t)δt.

[1892] I claim that, a plain English sentence can be used to describethe finite differential equation of motion representing approximatelythe Schrödinger equation, if “sentence pattern of physical and/ormathematical rules”, which is a data structure for knowledgerepresentation disclosed in the present invention, is made use of. Thatis,

[1893] _RULE_ Schrödinger equation _states_if(the wave function of asystem composed of n microscopic particles with their own masses isψ_(n)(x₁,y₁,z₁,x₂,y₂,z₂, . . . x_(n),y_(n),z_(n),t), and the potentialof the system is V_(n), at time =t;_)then{at time=t+δt, the wavefunction of a system composed of n microscopic particles with their ownmasses, ψ_(n)(x₁,y₁,z₁, . . . x_(n),y_(n),z_(n),t+δt), is given by,

ψ_(n)(x ₁ ,y ₁ ,z ₁ , . . . ,x _(n) ,y _(n) ,z _(n) ,t+δt)=ψ_(n)(z ₁ ,y₁ ,z ₁ ,t)+[2π/(ih)]H _(n)ψ_(n)(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . ,x_(n) ,y _(n) ,z _(n) ,t)δt.

[1894] , where ‘H_(n)’ is the Hamiltonian determined using the masses ofthe microscopic particles and V_(n);_};_(—)

[1895] Thus, the Schrödinger equation can be covered by anobject-oriented knowledge base system disclosed in the presentinvention. I claim that, this statement represents a kind law ofcausality. The term ‘causality’ means the relationship between a causeand the result. The cause of this rule of causality is of course,

[1896] “the wave function of a system composed of n microscopicparticles with their own masses is ψ_(n)(x₁,y₁,z₁,x₂,y₂,z₂, . . .,x_(n),y_(n),z_(n),t), and the potential of the system is V_(n),”and,the effect of this rule of causality is of course,

[1897] “the wave function of a system composed of n microscopicparticles with their own masses, ψ_(n)(x₁,y₁,z₁, . . .x_(n),y_(n),z_(n),t+δt), is given by,

ψ_(n)(x ₁ ,y ₁ ,z ₁ , . . . ,x _(n) ,y _(n) ,z _(n) ,t+δt)=ψ_(n)(z ₁ ,y₁ ,z ₁ ,t)+[2π/(ih)]H _(n)ψ_(n)(x ₁ ,y ₁ ,z ₁ ,x ₂ ,y ₂ ,z ₂ , . . . ,x_(n) ,y _(n) ,z _(n) ,t)δt.

[1898] , where ‘H_(n)’ is the energy user determined using the masses ofthe microscopic particles and ‘V_(n)’”.

[1899] The contrary of this proposition is also true. The causedescribed here determines uniquely the result described here, withoutfail.

[1900] I disclose a scheme with which the Newton's philosophy can beinterpretation in an absolutely through manner. At first, let us assumethat the world, including the planets in the universe, the earth and thesun, and atomic and/or molecular systems, can be ultimately decomposedinto mutually interacting microscopic particles, described by ‘matterwaves’. And let us regard the Schrödinger equation, as a rule ofcausality describing the time evolution of the system of the ‘matterwaves’ of the microscopic particles; It should be noted, of course, thatSchrödinger equation covers only little part of the real world, becausethe calculation to solve a Schrödinger equation of realistic sizedsystems is usually to heavy to processed by existing digital computes.As a matter of fact, only in special ideal cases in very narrow range ofengineering and/or science, Schrödinger equation is useful. In manycases, molecular system with only several to several ten's of atoms canbe treated as the target of computer simulation on the basis of aSchrödinger equation.

[1901] Schrödinger equation is an equation of motion for ‘matter waves’.It is clear that this is another example of the ‘Law-of-Causality andModel-of-the-World architecture’.

[1902] According to my schema of the ‘Law-of-Causality andModel-of-the-World architecture’ the philosophy of Schrödinger equationcan be summarized in a sentence that “Quantum Model-of-the-World worksaccording to the Schrödinger equation”. The system of forms of knowledgerepresentations of an object-oriented knowledge base system disclosed inthe present invention is suitable to embody the ‘Law-of-Causality andModel-of-the-World architecture’.

[1903] Newton's equation of motion and the Schrödinger equation can bethus interpreted in a unified way, when my ‘Law-of-Causality andModel-of-the-World architecture’ is adopted.

[1904] There exist plenty of ways to explain and interpret the conceptof the ‘uncertainty principle’ proposed by Heisenberg even today. Thediscussion that has been given by me is only one of them. However, Ibelieve that the discussion that has been given by me is a best way toexplain quantum dynamics.

3.3.6. Relativistic Schrödinger Equation Represented Using a KnowledgeRepresentation System of an Object-Oriented Knowledge Base Disclosed inthe Present Invention

[1905] The discussion about the Schrödinger equation given above isabout the quantum theory for non relativistic electrons. That is, thediscussion about the Schrödinger equation given above is about thequantum theory for electrons whose traveling velocity is much less thatthe velocity of light, ‘c’,

c=3.00×10⁸ meter/second.

[1906] The equation of motion for a relativistic electron is given bywhat is called the Dirac equation,

H _(D) ψ=[ih/(2π)](∂/∂t)ψ,

[1907] where, ‘ψ’ is what is called a spinor. In the case of oneelectron system, H_(D) is given by,

H _(D) =[h/(2π)]c Σ _(n=1˜3)γ₄γ_(n) [∂x _(n)−{2πie/(hc)A _(n) }+V+mc²γ₄,

[1908] where, ‘γ2 is a matrix whose elements are defined as, for n =1˜3, γ _(njk) = 0, for j = 1˜2, k = 1˜2, γ _(njk) = −is_(njk) for j =1˜2, k = 3˜4, γ _(njk) = is_(njk) for j = 3˜4, k = 1˜2, γ _(njk) = 0,for j = 3˜4, k = 3˜4,

[1909] where, ‘s_(n)’ are what is called the three Pauli matrices, and γ_(4jk) = I_(jk), for j = 1˜2, k = 1˜2, γ _(4jk) = 0 for j = 1˜2, k =3˜4, γ _(4jk) = 0 for j = 3˜4, k = 1˜2, γ _(4jk) = −I_(jk), for j = 3˜4,k = 3˜4,

[1910] where ‘I’ is the 2×2 unity matrix, and, ‘A’ is the vectorpotential of radiation.

[1911] I claim that, a plain English sentence can be used to describethe finite differential equation of motion representing approximatelythe Dirac equation, if “sentence pattern of physical and/or mathematicalrules”, which is a data structure for an object-oriented knowledgesystem disclosed in the present invention, as,

[1912] _RULE_ Dirac equation _states_if(the spinor of a system composedof a microscopic particles with its own mass is ‘ψ(t)’, and thepotential of the system is ‘V’, and the vector potential of theradiation is ‘A’, at time=t;_)then{at time=t+δt, the spinor of thesystem, ‘ψ(t+δt)’, is given by,

ψ(t+δt)=ψ(t)+[2π/(ih)]H _(D) δt

[1913] , where HD is the relativistic energy user determined using themasses of the microscopic particles, V, and A;_};_(—)

[1914] Of course, the step of logics are divided into infinitely finesteps when δt is made infinitively small, and in this point, equation ofmotion described by using a finite differential equation is differentfrom daily used syllogism, which covers only finite steps. In general,an equation of motion in a form of differential equation, includingNewton's equation of motion, the Schrödinger equation, and, Diracequation, can describe infinite numbers of steps of chain of causality.However, in a usual computer simulations, the value of ‘δt’ is not madeinfinitively small, but is made sufficiently small. Therefore, anequation of motion is also described as a daily used syllogism. Ofcourse, however, the characters, ‘∞’ and/or ‘1/∞’ may be used in acomputer simulation.

3.3.7. Fundamental Mathematics Represented Using a KnowledgeRepresentation System of an Object-Oriented Knowledge Base Disclosed inthe Present Invention

[1915] Thus far, the discussion for physics has been given. Here, Idiscuss the knowledge representation of mathematics. Many propositionsin mathematics can be described using “sentence pattern of physicaland/or mathematical rules”. For example, there is a proposition used aslemma 1.1 which appear §1 in {circle over (∘)}“Kaiseki-Nyumon I”, as,

[1916] “for any two real numbers, ‘a’, and ‘b’(a<b), there exist a realnumber, ‘c’, which satisfies, a<c<b.”

[1917] This proposition can be interpreted using a sentence described in“sentence pattern of physical and/or mathematical rules”, as

[1918] _RULE_ lemma 1.1 which appear §1 in {circle over(∘)}“Kaiseki-Nyumon I” _states_if(‘a’ and ‘b’ are real numbers and theysatisfy a<b;_)then{a real number ‘c’ exists which satisfiesa<c<b;_};_(—)

[1919] In general,

[1920] Under the condition, that ‘x’ is a variable moving on elements ofa set ‘X’, and that ‘P(x)’ represents an attribute of ‘x’, then, aproposition, (let us call here this proposition, Px), “For all x∈X, P(x)is satisfied”

[1921] is translated into a sentence in “sentence pattern of physicaland/or mathematical rules”,

[1922] _RULE_ Px _states_if(x∈X;_)then{P(x) is true;_};_(—)

[1923] Under the same condition, a proposition (let us call here thisproposition, Py) “At least one x∈X exists which satisfies P(x)”

[1924] is translated into a sentence in “sentence pattern of physicaland/or mathematical rules”,

[1925] _RULE_ Py _states_if(x∈X and P(x);_)then{At least one xexists;_};_(—)

[1926] It should be noted that many lemmas in mathematics can bedescribed by combining these two patterns of lemma.

3.3.8. @[Algorithm of Sentence Based Object-Oriented HypotheticalSyllogism]

[1927] <<Lexical Definition of @[algorithm of sentence basedobject-oriented hypothetical syllogism]>> Here, I define recursively the

[1928] @[algorithm of sentence based object-oriented hypotheticalsyllogism], which is an algorithm with which hypothetical syllogism iscarried out on the basis of sentence, as follows:

[1929] If

[1930] “RULE_ ** _states_if(proposition 1)then{proposition 2};_”

[1931] is true, (i.e. major premise)

[1932] and

[1933] proposition 1 is derived from proposition 3 using @[algorithm ofsentence based object-oriented categorical syllogism] and/or @[algorithmof sentence based object-oriented hypothetical syllogism], (i.e. thefirst minor premise)

[1934] and,

[1935] proposition 4 is derived from proposition 2 using @[algorithm ofsentence based object-oriented categorical syllogism] and/or @[algorithmof sentence based object-oriented hypothetical syllogism] (i.e. thesecond minor premise)

[1936] then,

[1937] “_RULE_ ***** _states_if(proposition 3 )then{proposition 4};_”isalso true (i.e. the conclusion).

[1938] A quasi-C code of Formula. 16 outlines the procedure to judgewhether a proposition is a “conclusion of a ‘hypothetical syllogismdescribed by @[algorithm of sentence based object-oriented hypotheticalsyllogism]’ for a major premise” or not.

Lexical Definition of ‘Means for Carrying Out Sentence BasedObject-Oriented Hypothetical Syllogism’

[1939] @[Algorithm of sentence based object-oriented hypotheticalsyllogism] and/or something that stores the information of it, is a‘means for carrying out sentence based object-oriented hypotheticalsyllogism’.

[1940] As is clear from the lexical definition of @[algorithm ofsentence based object-oriented hypothetical syllogism], ‘means forcarrying out sentence based object-oriented hypothetical syllogism’ isdefined on the basis of ‘means for carrying out sentence basedobject-oriented categorical syllogism’. This is schematically shown inFIG. 16.

[1941] @[Algorithm of sentence based object-oriented hypotheticalsyllogism] is a mechanism of reasoning used in an object-orientedknowledge base system disclosed in the present invention.

[1942] On the basis of “sentence pattern of physical and/or mathematicalrules” used as a rule of an object-oriented knowledge base systemdisclosed in the present invention can be multiplied, and, as theresult, many sentences in “sentence pattern of physical and/ormathematical rules” can be obtained, if @[algorithm of sentence basedobject-oriented hypothetical syllogism] is repeatedly used. And the manysentences in “sentence pattern of physical and/or mathematical rules”thus obtained can be used as a ‘rule’ of an object-oriented knowledgebase system disclosed in the present invention.

[1943] I adopt an opportunistic reasoning model as the problem solvingmodel, when one chooses useful knowledge from the knowledge source. Andit is recommended that, one should use @[algorithm of narrowing down thetarget ‘descriptors’ and/or target ‘names-of-classification-items’]and/or should use @[algorithm of fusing propositions], if and when toomany keys to be used as a rule are retrieved. And it is recommended thatone should use @[algorithm of broadening out the target ‘descriptors’and/or target ‘names-of-classification-items’], if and when too lesskeys to be used as a rule are retrieved.

[1944] An example in which a problem is solved (example of theoremproving) on the basis of @[algorithm of sentence based object-orientedhypothetical syllogism], @[algorithm of sentence based object-orientedcategorical syllogism] and the opportunistic reasoning model will beshown in the section of “§3.6. Example in which an Object-orientedknowledge base system Disclosed in the Present invention is applied to aPractical case in Solving a Problem in Metallurgical Physics”.

3.3.9. “Sentence pattern of Function”

[1945] Next, keys recording data in the form of “If the ‘input’ of afunction is ˜, then the ‘output’ is ˜” will be discussed as anotherexample of the “sentences that store data used as rules”. Functions usedin C language and/or functions used in mathematics can be formalized byusing the data structure, which I name “sentence pattern of function”.

[1946] <<Lexical Definition of “sentence pattern of function”>> I callthis data structure, _FUNCTION_ ***** _translate_INPUT_ ****_into_OUTPUT_***,

[1947] the

[1948] “sentence pattern of function”,

[1949] where ‘*****’ is the name of a function and/or the name of an‘algorithms-of-processes’, ‘****’ is the inputs, and ‘***’ is theoutputs. The ‘****’ and the ‘***’ are a permutation of arguments of thefunction. A proposition may be used as an argument. And the ‘****’ andthe ‘***’ describes situation before and after the matter described bythe ‘algorithms-of-processes’ happens. I claim in the present inventionthat this data structure can be used to formulate even ‘individualfunctions’, which are in many cases a pure black box.

Lexical Definition of ‘Means for Describing a Function Used as a Rule’

[1950] Sentence described in “sentence pattern of function” and/orsomething that stores the information of it, is a ‘means for describinga function used as a rule’.

[1951] In the present invention, examples in which an object-orientedknowledge base system disclosed in the present invention is used will beshown later, wherein, the name of a subroutine, and/or the name of acomputer program, and/or the name of an unit operation described in amanual, is used as the name of a function used in the ‘means fordescribing a function used as a rule’ (See “§3.4. CAD for Coding ofComputer programs”, and “§3.5. On-Line Manual and/or On-Line Help ofMachines”, and See FIG. 7).

Lexical Definition of ‘Means for Describing the Function of a Verb’

[1952] Sentence in “sentence pattern of function” and/or something thatstores the information of it, is a ‘means for describing the function ofa verb’.

Lexical Definition of ‘Presupposition’ of a ‘Hypothetical Proposition’Described in “Sentence Pattern of Function”

[1953] In the present invention, I regard a sentence in “sentencepattern of function” a kind of a ‘hypothetical proposition’. If and whenthe ‘hypothetical proposition’ is described in the “sentence pattern offunction”,

[1954] _FUNCTION_ ***** _translate_INPUT_ **** _into_OUTPUT_ ***;_,then, the ‘presupposition’ of the ‘hypothetical proposition’ means theproposition in the field below ‘_translate_INPUT_’, i.e. the‘presupposition’ is the ‘****’.

Lexical Definition of ‘Consequence’ of a ‘Hypothetical Proposition’Described in “Sentence Pattern of Function”

[1955] And if and when the ‘hypothetical proposition’ is described in“sentence pattern of function”,

[1956] _FUNCTION_ ***** _translate_INPUT_ **** _into_OUTPUT_ ***;_,then, the ‘consequence’ of the ‘hypothetical proposition’ means theproposition in the field below ‘into_OUTPUT_’ i.e. the ‘consequence’ isthe ‘***’.

[1957] This “sentence pattern of function” can be used to represent thekey of a data used as a rule in an object-oriented knowledge base systemdisclosed in the present invention.

[1958] If one wants to use systematically a sentence in “sentencepattern of function” as a rule in an object-oriented knowledge basesystem disclosed in the present invention, it is recommended that themaker of the contents of the knowledge base should use as higher class‘names-of-classification-items’ as possible as the verbs in ‘*****’,‘****’, and/or ‘***’. And it is recommended that he should use asbroader ‘descriptors’ as possible, as ‘subject-words (S)’, ‘object-words(O)’, and/or ‘indirect-object-word (I.O)’ in ‘*****’, ‘****’, and/or‘***’. And it is recommended that he should use as narrower‘descriptors’ as possible, as ‘complement-words (C)’ in ‘*****’, ‘****’and/or ‘***’ of, _FUNCTION_ ***** _translate_INPUT_ **** _into_OUTPUT_***;_,

[1959] If the maker of the contents of the knowledge base uses thehighest ‘names-of-classification-items’ and/or broadest ‘descriptors’ aspossible, then, the power of expression of the rule described in asentence in “sentence pattern of function” becomes general anduniversal.

[1960] And if and when a user of the knowledge base system wants toapply such a general and universal rule described by using a sentence in“sentence pattern of function” to a practical and specific case, it isrecommended that the systematic hierarchical structures of‘classification table’ and/or ‘ideal thesaurus’ should be made full useof by the user of the knowledge base system to retrieve a general anduniversal rule which is applicable to his own case. In other words, theuser has only to consult the ‘classification tables’ and/or the ‘idealthesauruses’ to see whether higher class and/or lower class‘names-of-classification-items’, and broader and/or narrower‘descriptors’ are used in a rule stored in an object-oriented knowledgebase system disclosed in the present invention to retrieve useful rules.The detail of this issue will be given in the section of “§3.3.11.Algorithm of Association and Reasoning (i.e. algorithm of ‘Inferencemechanism’) of an Object-oriented knowledge base system disclosed in thePresent invention”.

3.3.10. “Sentence Pattern of Instances of Solving Problems” LexicalDefinition of “Sentences that Store Data About Instances of SolvingProblems”

[1961] A sentence describing important parts of the log recorded duringa problem is solved on a knowledge base system, is a “sentence whichstores data about instances of solving problems”

Lexical Definition of “Sentence Pattern of Instances of SolvingProblems”

[1962] The data structure of “sentence pattern of instances of solvingproblems” is defined using quasi-C code as follows:

[1963] _SENTENCE_PATTERN_OF_INSTANCE_of_SOLVING_(—)

[1964] _PRESENT_IDENTIFICATION_NUMBER_ the identification number of thepresent key in the “sentence pattern of instances of solving problems”.

[1965] _PREVIOUS_IDENTIFICATION_NUMBER_ the identification number of thelarge-grained less strict instances of solving problems preceding thepresent one if any

[1966] _SOLVED_PROPOSITION_ proved proposition

[1967] _ORIGINAL_TITLE_ original title given by the man who solved thisproblem

[1968] _ENGLISH_TITLE_ English title translated from the original one

[1969] _ORIGINAL_ABSTRACT_ an outline of the present key described usingsentences in “sentence pattern of instances of solving problems”

[1970] _ENGLISH_ABSTRACT_ an outline translated into English

[1971] _AUTHOR_ name of the man who solved the problem and hisaffiliation

[1972] _COPYRIGHT_ copyright and its owner if it exists

[1973] _PATENT_ patent, its type, and its owner

[1974] _NAME_OF_LITERATURE_ name of literature of the record of thepresent key

[1975] _ISBN_OF_LITERATURE_ ISBN of the literature

[1976] _VOLUME_OF_LITERATURE_ volume of the literature

[1977] _NUMBER_OF_LITERATURE_ No. of the literature

[1978] _YEAR_OF_PUBLICATION_OF_LITERATURE_ year of publication of theliterature

[1979] _PAGR_OF_LITERATURE_ page of the literature at which the recordis present

[1980] _NAME_OF_CONFERENCE_ name of the conference at which the recordis presented

[1981] _NUMBER_OF_CONFERENCE_ serial number of the conference

[1982] _PLACE_OF_CONFERENCE_ place at which the conference was held

[1983] _LANGUAGE_of_THIS_KEY_ language in which this key is written

[1984] _PRINCIPAL_ITEMofCLASSIFICATION_ principal‘names-of-classification-items’ of this key

[1985] _PRINCIPAL_Noun_KW principal ‘descriptors’ of this key

[1986] _ACCURACY_of_THIS_INSTANCE_of_SOLVING_ the degree of strictnessof the present instance of solving _main( )_(—) { the first step ofopportunistic reasoning second step of opportunistic reasoning . . i'thstep of opportunistic reasoning . . last step of opportunistic reasoning}

[1987] where, the data structure of the i'th step of opportunisticreasoning is given by,

[1988] _A_STEP_of_OPPORTUNISTIC_REASONING_(—)

[1989] _DIRECTION_=_ forward or backward?

[1990] _PROVED_HYPOTHETICAL_PROPOSITION_=_ hypothetical propositionproved in the present step

[1991] _USED_HYPOTHETICAL_PROPOSITION_=_ hypothetical proposition usedin reasoning of the present step

[1992] _USED_Noun_KW_=_ ‘descriptors’ used in the retrieval ofhypothetical proposition used in reasoning of the present step

[1993] _USED_ITEMofCLASSIFICATION_=_ ‘names-of-classification-items’used in the retrieval of hypothetical proposition used in reasoning ofthe present step

Lexical Definition of ‘Means for Storing Data About Instances of SolvingProblems’

[1994] Sentence in “sentence pattern of instances of solving problems”and/or something that stores the information of it, is a ‘means forstoring data about instances of solving problems’.

[1995] It is recommended that keys in “sentence pattern of instances ofsolving problems” should be registered in an object-oriented knowledgebase system disclosed in the present invention only when the problem hasbeen proved successfully regardless of the size of the ‘grain’ of therules used in solving problems, therefore regardless of the strictnessof the rules used in solving problems.

[1996] If the i'th step of opportunistic reasoning is less strict, it isrecommended that all the steps should be proved again strictly by morestrict opportunistic reasoning, and that only the result should berecorded as the key described in the “sentence pattern of instances ofsolving problems”.

[1997] It is recommended that the field of_PREVIOUS_IDENTIFICATION_NUMBER_ of another key in the “sentence patternof instances of solving problems” should be filled with theidentification number that was filled with in the field of_PRESENT_IDENTIFICATION_NUMBER_ of the original key before thereconsideration.

BEST MODE FOR CARRYING OUT THE INVENTION 3.3.11. Algorithm ofAssociation and Reasoning (i.e. Algorithm of ‘Inference Mechanism’) ofan Object-Oriented Knowledge Base System Disclosed in the PresentInvention 3.3.11.1. Overview of the Algorithm for ‘Inference Mechanism’

[1998] <<Lexical Definition of an inference mechanism used in anobject-oriented knowledge base system disclosed in the presentinvention>> The mechanism with which association and/or reasoning iscarried out to prove a theorem, in an object-oriented knowledge basesystem disclosed in the present invention, is an inference mechanismused in the object-oriented knowledge base system disclosed in thepresent invention.

Lexical Definition of ‘Means for Carrying out an Inference’

[1999] Sentence describing “an inference mechanism used in anobject-oriented knowledge base system disclosed in the presentinvention” and/or something that stores the information of it is a‘means for carrying out an inference’.

[2000] As mentioned before, the style of the reasoning of the ‘means forcarrying out an inference’ is an opportunistic reasoning. Each step ofopportunistic reasoning is either forward reasoning and/or backwardreasoning (See FIG. 9).

[2001] The main routine of the quasi-C code representing a recommendedalgorithm of association and reasoning (i.e. a recommended algorithm ofinference mechanism) used in an object-oriented knowledge base systemdisclosed in the present invention, is described in Formula. 2 , inwhich main part of an opportunistic reasoning is implemented (See FIG. 9and the explanation which has been given thereof in the presentinvention). And the subroutine used in the case when a forward reasoningis to be carried out as a step of the opportunistic reasoning, isdescribed in Formula. 3, using a quasi-C code. And the subroutine usedin the case when a backward reasoning is to be carrying out as a step ofthe opportunistic reasoning, is described in Formula. 4, using a quasi-Ccode.

[2002] This ‘means for carrying out an inference’ is a kind of means for‘theorem proving’. In other words, for example, the ‘means for carryingout an inference’ is used as a tool to give an answer to a questionasked by users of the system (See FIG. 1). And the body of theinformation of the ‘means for carrying out an inference’ is stored in a‘means for storing knowledge base system’ (See FIG. 1 ), (for example,is stored in a ‘hard disk’ of a computer). And as the platform machineon which the ‘means for carrying out an inference’ works, what I call‘digital computing system’, (See FIG. 1) to be defined just below, isused. This ‘digital computing system’ (for example, a computer) isoperated by a man and/or by plurality of other ‘digital computingsystems’. During this operation, an ‘input device’ (for example, a ‘keyboard’) and/or an ‘output device’ (for example, a ‘display’) is used asan interface. For example, if and when a man uses an object-orientedknowledge base system disclosed in the present invention by operatingthe ‘digital computing system’, then he will input the question via the‘input device’ to the ‘digital computing system’ first, and the ‘digitalcomputing system’ finally gives the answer to the man via the ‘outputdevice’. Wherein, an ‘object-oriented knowledge base’, which is alsostored on a ‘means for storing knowledge base system’, provides ‘rules’and/or ‘facts’, etc., which are used as a knowledge on the basis ofwhich the ‘means for carrying out an inference’ makes a reasoning, onthe ‘digital computing system’. In this case, the reasoning is carriedout according to the instructions given via by the user and/or by theplurality of other ‘digital computing systems’. This situation isschematically shown in FIG. 1.

Lexical Definition of ‘Digital Computing System’

[2003] A ‘digital computing system’ is a system that is based on is adigital computing technology. Strictly speaking, a system based on awired logic control as well as a system that uses a CPU (CentralProcessing Unit) and micro programs, is a ‘digital computing system’.Hybrid of them is also a ‘digital computing system’. For example, acomputer, such as a personal computer, a work station, and a mainframecomputer, is kid of a ‘digital computing system’. And a home electricappliance with digital technologies is a kind of a ‘digital computingsystem’. But in the present invention, I regard a ‘digital computingsystem’, such as a system based on a wired logic control and/or well asa home electric appliance with digital technologies, as a kind ofcomputer.

[2004] This definition is schematically shown in FIG. 22.

[2005] As computers on which the knowledge base system disclosed in thepresent invention is to be embodied, I recommend ones which supportsoperation systems such as Windows 95, Windows NT, UNIX, and, Linux. Thealgorithm of the present knowledge base system is essentially very slim,and can be implemented on a usual commercially available personalcomputers including pc-at(dos/v) machines, if the size of the knowledgebase is small.

[2006] As such ‘digital computing system’, PC's (Personal computers)with such specification, for example, can be used;

[2007] main processor: Pentium© processor 100 MHz, VRT.

[2008] memory: 40 MB.

[2009] ROM: 128 KB flush ROM, plug and play 1.0a,APM1.1.

[2010] display: external color CRT 800×600 dots full color.

[2011] video RAM: 2 MB

[2012] key board: external 88 keys.

[2013] floppy disk drive:

[2014] hard disk drive:

[2015] CD-ROM drive:

[2016] serial interface: 1 channel (RS-232C, 9 pin D-sub),

[2017] parallel interface: 1 channel (Centronics, 25 pin D-sub, ECPcompatible)

[2018] CRT interface: 1 channel (15 pins D-sub)

[2019] PS/2 interface: 1 channel

[2020] head phone output interface: 1 channel, stereo mini jack)

[2021] audio input interface: 1 channel, stereo mini jack)

[2022] microphone input interface: 1 channel, monoral mini jack)

[2023] working environment: 5-35° C., humidity 30-80%

[2024] OS: Microsoft©Windows©95

[2025] Utility software: Microsoft©Word 2000, Microsoft©Excel95

[2026] It is recommended that a user of the present system and/or aknowledge engineer for the present system should work at a desk on wellfitted to their body. It is recommended that the center of the displayexist about 35 cm above the top of the desk on which they works (i.e.7.0 cm lower than the position of the eyes of them). And the distancedisplay between the eyes of them and the display is recommended to beset about 40 cm or more. When they operate a computer, they should relaxtheir necks and/or their shoulders in a minute every twenty minites by astreatching and moving joints of necks, shoulders, elbows, backs, nees,and/or fingers. It is recommended that space that is wide enough forsuch exercises to be carried out by them should be given around them. Itis recommended that they should rest their VDT (video display terminal)work for about 10 to 15 minutes every one hour. A light source of theroom should not be in direct range of their vison. In this 10 to 15minutes, it is recommended that they should move the whole of theirbody, for example by repeating sitting down and standing up a few times,for them to get a good blood circulation. It is recommended that the airof the room should be well conditioned, and the air condition must notabruptly stopped and/or abruptly started when they are in the room. Theair of the room should be well ventilated and circulated. It isrecommended that the humidity of the room should be kept around 60%. Itis recommended that video display terminal disorder should be prevented.

[2027] The key board and mouse of such PC's can be used as an ‘inputdevice’. The display of such PC's can be used as an ‘output device’.DynaBook Satellite Pro 430 is with Pentium processor (120 MHz) as theCPU, with the memory up to 48 MB, with hard disk drive 1.35 GB. Ofcourse, such PC's with equivalent spec and/or superior spec, provided byCOMPAC, GATEWAW, IBM, HITACHI, etc. are OK. A workstations and/or hostcomputers may be used to embody an object oriented knowledge base systemof the present invention. server A home electric appliance with digitaltechnologies is a kind of a ‘digital computing system’. Anobject-oriented knowledge base system disclosed in the present inventionmay be used in home electric appliances with digital technologies.

[2028] As the computer program with which the program for anobject-oriented knowledge base system disclosed in the present inventionis coded, I recommend the C languages and/or the C++ language. Machinelanguage is recommended to be used to store an object-oriented knowledgebase system disclosed in the present invention. Of course, FORTRAN ormachine language may be used. When these languages are to be usedon,Windows 95 and/or Windows NT, I recommend as a development system andtools, for example, the Microsoft Visual C++ Standard Edition (Version4.0), which covers not only command line C and/or C++ programming, butalso Windows programming using C, and/or C++. Of course it is possibleto implement the algorithm of association and reasoning disclosed in thepresent invention either only by using command line C and/or C++languages and/or by using Windows programming using C, and/or C++. It isrecommended that one should read {circle over (∘)}“Programming Windows95”, when a beginner wants to code a Windows program. The algorithmand/or data used in an object-oriented knowledge base system ofdisclosed in the present invention, may be stored in a ASIC.

[2029] {circle over (∘)}“Programming Windows 95 with MFC”, and {circleover (∘)}“Advanced Windows (Third Edition)” are a textbook recommendedto be read, if one wants to know how to use the Microsoft Visual C++Standard Edition (Version 4.0).

3.3.11.2. Main Routine 3.3.11.2.1. Data Input

[2030] In the main routine, at first, the system lets the user of anobject-oriented knowledge base system disclosed in the present inventioninput the proposition to be finally proven by using the knowledge basesystem (i.e. input the question). This corresponds to the sentence inFormula. 2,

[2031] Let_the_User_of_the_system_Input_(—)

[2032] the_Proposition_to_be_finally_proven_by_using_the_system

[2033] and_Copy_and_Paste_it_on_(—)

[2034] PropositionToBeFinallyProvenByUsingTheSystemMatrix_( )_;

[2035] <<Lexical Definition of an ‘input device’>> A device via whichusers of a ‘digital computing system’ and/or other ‘digital computingsystems’ transmit signals and/or messages to the ‘digital computingsystem’ is an ‘input device’. For example, a key board, microphone, apointing device such as a mouse, and, a image scanner, etc. may be usedas an ‘input device’ when a man uses the ‘digital computing system’.Devices for input-output interfaces, such as RS-232C, SCSI, USB,Centronics interface, modem, LAN interface board (i.e. Network InterfaceCard), and/or other hardware compatible with protocols for the internet,may be used as a ‘input device’, when other ‘digital computing systems’use the ‘digital computing systems’. When the proposition to be finallyproven by using an object-oriented knowledge base system disclosed inthe present invention, a computer on which the object-oriented knowledgebase system is installed, may be used either by a man, and/or by other‘digital computing system’, such as other personal computers connectedvia the internet and/or via a LAN (Local Aria Network) to the computerwhose object-oriented knowledge base system receives information. It isrecommended that in this case, the whole of and/or a part of anobject-oriented knowledge base system disclosed in the present inventionshould be installed also on the other digital computer.

[2036] <<Lexical Definition of an ‘output device’>> A device via whichusers of a ‘digital computing system’ and/or other ‘digital computingsystems’ receive signals and/or messages from the ‘digital computingsystem’ is an ‘output device’. For example, a display, and, a speaker,etc. may be used as an ‘output device’, when a man uses the ‘digitalcomputing system’. Devices for input-output interfaces, such as RS-232C,SCSI, USB, Centronics interface, modem, LAN interface board (i.e.Network Interface Card), and/or other hardware compatible with protocolsfor the internet, and, etc. may be used as an ‘output device’ when other‘digital computing systems’ use the ‘digital computing systems’. Whenthe proposition is to be finally proven by using an object-orientedknowledge base system disclosed in the present invention, a computer onwhich the object-oriented knowledge base system is installed, may beused either by a man, and/or by other ‘digital computing system’, suchas other personal computers connected via the internet and/or via a LAN(Local Aria Network) to the computer whose object-oriented knowledgebase system receives information. It is recommended that in this case,the whole of and/or a part of an object-oriented knowledge base systemdisclosed in the present invention should be installed also on the otherdigital computer.

[2037] In the main routine, second, if the given the proposition to befinally proven by using the object-oriented knowledge base system, isdescribed in natural language, then, it is recommended that theproposition should be translated into a proposition described in formaldata structures using data structures defined in the present invention,i.e. into a proposition described in “sentence pattern of physicaland/or mathematical rules” and/or into a proposition described in“sentence pattern of function”, etc. In addition, it is recommended thatsuch a propositions in “sentence pattern of physical and/or mathematicalrules” and/or “sentence pattern of function”, etc., should be describedin “sentence pattern of one of five basic sentence patterns of Englishgrammar”, to make it easier for the present system to recognize the ‘S’,‘V’, ‘C’, ‘O’, ‘I.O’, or ‘D.O’ in the propositions. A knowledge engineercan make such a translation.

[2038] <<Lexical Definition of a knowledge engineer>> In the descriptionof the present invention, A knowledge engineer is an engineer who hasthe full knowledge about an object-oriented knowledge base systemdisclosed in the present invention.

[2039] Any person whose intelligence is as high as and/or is competitiveto those of successful applicants for written examination for seniorofficial of national public service of Japan (Jyou-kyu kokka koumuinshikenn goukakusha) has enough competence to work as a knowledgeengineer.

3.3.11.2.2. Formalization of the Inputted Data

[2040] If and when the given proposition is a hypothetical proposition,then there is no problem here at the spot. If and when the givenproposition is a categorical propositions, then the given propositionmust be treated as a hypothetical proposition whose ‘presupposition’ isempty. A knowledge engineer can do such translations.

[2041] This issue corresponds to the sentence in Formula. 2,

[2042] Translate_the_Proposition_in_(—)

[2043] PropositionToBeFinallyProvenByUsingTheSystemMatrix

[2044] into_a_formal_sentence_which_is_described_by_using_(—)

[2045] “sentence pattern of physical and/or mathematical rules”,_or_(—)

[2046] “sentence pattern of function”,_and_(—)

[2047] “sentence pattern of one of five basic sentence patterns ofEnglish grammar”

[2048] and_Copy_and_Paste_the_formal_sentence_on_(—)

[2049] FormalizedPropositionToBeFinallyProvenByUsingTheSystemMatrix_();_.

3.3.11.2.3. Step by Step Opportunistic Reasoning 3.3.11.2.3.0. OverviewLexical Definition of Each Step of Opportunistic Reasoning

[2050] Opportunistic reasoning is usually carried out step by step. Ifand when the opportunistic reasoning is to be carried out in arecommended way, then, each step of opportunistic reasoning (See FIG. 9,and FIG. 11) should be carried out in an execution of the loop body ofthe ‘while( ){}’ iteration statement in Formula. 2. Here ‘the loop bodyof the ‘while( ){}’ iteration statement’ means the sentences (i.e. thefunctions) described in parentheses, ‘{’ and ‘}’ of ‘while( ){}’. Aswill be understood if one watches carefully Formula. 2, either forwardreasoning and/or a backward reasoning is carried out in each executionof the loop body.

[2051] Roughly speaking, in each step of opportunistic reasoning,

[2052] First, “means for getting ‘rules-for-reasoning’” (See FIG. 11) iscarried out to obtain ‘rules’ from an object-oriented knowledge base(See FIG. 1), to be used as the basis on which reasoning should becarried out.

[2053] Second, if too many ‘rules-for-reasoning’ are obtained by meansof “means for getting ‘rules-for-reasoning’”, then, “means for avoidingcombinatorial explosion when too many number of ‘rules-for-reasoning’are retrieved” (See FIG. 11) should be carried out to prevent thepuncture of processing capacity of the ‘digital computing system’ (SeeFIG. 1).

[2054] Third, if too less ‘rules-for-reasoning’ are obtained by means of“means for getting ‘rules-for-reasoning’”, and as the result,opportunistic reasoning can not be progressed any more, then, “means formaking more exhaustive retrieval when too less number ofrules-for-reasoning are retrieves” (See FIG. 11) should be carried outto escape from the deadlock.

[2055] Forth, a reasoning is carried out, and if and when, the finalanswer is not obtained in the present step of opportunistic reasoning,then, “means for determining hypothetical propositions which are to beused as the target of the next step of opportunistic reasoning” (SeeFIG. 11) should be carried out, as the preparation for the next step ofopportunistic reasoning.

[2056] I will give below more detailed explanation for

[2057] “means for getting ‘rules-for-reasoning’”,

[2058] “means for avoiding combinatorial explosion when too many numberof ‘rules-for-reasoning’ are retrieved”,

[2059] “means for making more exhaustive retrieval when too less numberof rules-for-reasoning are retrieves”,

[2060] and,

[2061] “means for determining hypothetical propositions which are to beused as the target of the next step of opportunistic reasoning”

[2062] by using FIG. 11, as follows:

[2063] Before all, it should be noted that, as mentioned before, the aimof each step of opportunistic reasoning is to prove a ‘hypotheticalproposition which is the target of the present step of opportunisticreasoning’ (See FIG. 10).

Lexical Definition of the ‘Hypothetical Proposition Which is the Targetof the Present Step of Opportunistic Reasoning’ Which is Dealt with whenthe ‘Present Step’ Equals to the ‘First Execution of the Loop Body inthe ‘While( ){}’ Iteration Statement’

[2064] Generally speaking, a ‘hypothetical proposition that is thetarget of the present step of opportunistic reasoning’ is a hypotheticalproposition that is tried to be logically proved in the present step ofopportunistic reasoning. Here, I define a ‘hypothetical propositionwhich is the target of the present step of opportunistic reasoning’which is determined, before the control of the computer rushes into the‘while( ){}’ loop, and is used as the target in the first step in the‘while( ){}’ loop. The proposition which is obtained by formalizing ‘theformalized proposition to be finally proved using the present system’,should be used as the ‘hypothetical proposition which is the target ofthe opportunistic reasoning carried out in the first execution of theloop body in the ‘while( ){}’ iteration statement’. This sentencecorresponds to the sentence in Formula. 2 , ^(′)hypothetical_proposition_which_is_the_target_of_the_present_step_of_opportunistic_reasoning^(′) = FormalizedPropositionToBeFinallyProvenByUsingTheSystemMatrix  •; _.  

[2065] As will be described in the lexical definition of ‘rules forreasoning’ given later in the present invention, I call,

[2066] a rule on the basis of which a reasoning is carried out in a stepof opportunistic reasoning to prove a ‘hypothetical proposition which isthe target of the present step of opportunistic reasoning’,

[2067] a ‘rule-for-reasoning’.

Lexical Definition of “Means for Getting ‘Rules-for-Reasoning’”

[2068] “Means for getting ‘rules-for-reasoning’” is a component of eachstep of opportunistic reasoning (See FIG. 11) which is carried out firstin the step of opportunistic reasoning.

[2069] “Means for getting ‘rules-for-reasoning’” is a tool used in‘means for carrying out an inference’ (See FIG. 1) with which toretrieve ‘rules-for-reasoning’ from the ‘rules’ in an ‘object-orientedknowledge base’ (See FIG. 1).

[2070] Here now, let me add some explanation of “means for getting‘rules-for-reasoning’”, in more detailed way: There are two recommendedways in which “means for getting ‘rules-for-reasoning’” should beembodied.

[2071] One recommended way with which to carry out “means for getting‘rules-for-reasoning’”, is an indirect way in which first, rules whichcan be possibly used as a ‘rules-for-reasoning’ in a step ofopportunistic reasoning are retrieved from an ‘object-oriented knowledgebase’. And second, only just the ‘rules-for-reasoning’ used for the stepof opportunistic reasoning are picked up from the rules which can bepossibly used as a ‘rules-for-reasoning’. Thus, ‘rules-for-reasoning’ isobtained in a indirect way. According to a lexical definition givenlater in the present invention, a rule which can be possibly used as a‘rules-for-reasoning’ is called a “candidates of the‘rules-for-reasoning’”, in the present invention.

[2072] A recommended indirect way to carry out “means for getting‘rules-for-reasoning’”, comprises (See FIG. 11),

[2073] 1) the first step, in which ‘descriptors’ to be used in theretrieval of the third step is tried to be obtained, by carrying out,

[2074] “means for getting ‘descriptors’ that are used to make a query toget the “candidates of the ‘rules-for-reasoning’””,

[2075] (For detail of the first step,. see “§3.3.11.2.3.1.1. Search of‘Descriptors’ to be used for a “Retrieval of the ‘Hypotheticalpropositions possibly used in Forward reasoning’””, and see“§3.3.11.2.3.2.1. Search of ‘Descriptors’ to be used for a “Retrieval ofthe ‘Hypothetical propositions Possibly used in Backward reasoning’””),

[2076] 2) the second step, in which ‘names-of-classification-items’ tobe used in the retrieval of the third step is tried to be obtained, bycarrying out,

[2077] “means for getting ‘names-of-classification-items’ that are usedto make a query to get the “candidates of the ‘rules-for-reasoning’””,

[2078] (For detail of the second step, see “§3.3.11.2.3.1.2. Search of‘Names-of-Classification-items’ to be used for a “Retrieval of the‘Hypothetical propositions Possibly used in Forward reasoning’””, andsee “§3.3.11.2.3.2.2. Search of ‘Names-of-Classification-items’ to beused for a “Retrieval of the ‘Hypothetical propositions Possibly used inBackward reasoning’””)

[2079] 3) the third step, in which as the preparation for the fourthstep, “retrieval of the “candidates of the ‘rules-for-reasoning’””, iscarried out by using the ‘descriptors’ obtained and the first step andthe ‘rules-for-reasoning’ obtained in the second step (For detail of thethird step, see “3.3.11.2.3.4.1.1. Retrieval of “candidates of the‘rules-for-reasoning’””, and see “3.3.11.2.3.4.2.1. Retrieval of“candidates of the ‘rules-for-reasoning’””), and,

[2080] 4) the fourth step, in which,

[2081] “means for picking up only the ‘rules-for-reasoning’ from the“candidates of the ‘rules-for-reasoning’””,

[2082] is carried out on the basis of the output of the third step (Fordetail of the fourth step, see “§3.3.11.2.3.4.1.6. “Means for picking uponly the ‘rules-for-reasoning’ from the “candidates of the‘rules-for-reasoning’”””, and see “§3.3.11.2.3.4.2.6. “Means for pickingup only the ‘rules-for-reasoning’ from the “candidates of the‘rules-for-reasoning’”””).

[2083] The lexical definition of

[2084] “means for getting ‘descriptors’ that are used to make a query toget the “candidates of the ‘rules-for-reasoning’””,

[2085] the lexical definition of

[2086] “means for getting ‘names-of-classification-items’ that are usedto make a query to get the “candidates of the ‘rules-for-reasoning’””,

[2087] the lexical definition of

[2088] “retrieval of the “candidates of the ‘rules-for-reasoning’””,

[2089] and

[2090] the lexical definition of

[2091] “means for picking up only the ‘rules-for-reasoning’ from the“candidates of the ‘rules-for-reasoning’””,

[2092] will be given later in the present invention.

[2093] The other recommended way with which to carry out “means forgetting ‘rules-for-reasoning’”, is a direct way in which the‘rules-for-reasoning’ used in a step of opportunistic reasoning isretrieved from an ‘object-oriented knowledge base’. The procedure tocarry out this direct way is what I call “means for retrieving directlythe ‘rules-for-reasoning’”. The lexical definition of “means forretrieving directly the ‘rules-for-reasoning’” will be given later inthe present invention. For detail, see “§3.3.11.2.3.4.1.2. Means forRetrieving directly Hypothetical propositions to be used in forwardreasoning”, and see “§3.3.11.2.3.4.2.2. Means for retrieving directlyHypothetical propositions to be used in backward reasoning”.

Lexical Definition of “Means for Avoiding Combinatorial Explosion Whentoo Many Number of ‘Rules-for-Reasoning’ are Retrieved”

[2094] A measure to prevent the processing capacity of a ‘digitalcomputing system’ (See FIG. 1) from falling into puncture when too many“candidates of the ‘rules-for-reasoning’” are retrieved (i.e. whencombinatorial explosion occurs) during “means for getting‘rules-for-reasoning’” is carried out (See FIG. 11),

[2095] is a

[2096] “means for avoiding combinatorial explosion when too many numberof ‘rules-for-reasoning’ are retrieved”.

[2097] “Means for avoiding combinatorial explosion when too many numberof ‘rules-for-reasoning’ are retrieved” is a component of each step ofopportunistic reasoning. And “means for avoiding combinatorial explosionwhen too many number of ‘rules-for-reasoning’ are retrieved” is carriedout only when combinatorial explosion occurs during “means for getting‘rules-for-reasoning’” is carried out (See FIG. 11).

[2098] As recommended procedures with which “means for avoidingcombinatorial explosion when too many number of ‘rules-for-reasoning’are retrieved” is embodied in an object-oriented knowledge base systemdisclosed in the present invention, there exist three means (See FIG.11):

[2099] 1) ‘Means for narrowing down the target ‘descriptors’’ (Fordetail, see “§3.3.11.2.3.4.1.3. ‘Means for narrowing down the target‘descriptors” and ‘Means for narrowing down the target‘names-of-classification-items’’”, and see “§3.3.11.2.3.4.2.3. ‘Meansfor narrowing down the target ‘descriptors’’ and ‘Means for narrowingdown the target ‘names-of-classification-items’’”),

[2100] 2) ‘Means for narrowing down the target‘names-of-classification-items’’ (For detail, see “§3.3.11.2.3.4.1.3.‘Means for narrowing down the target ‘descriptors” and ‘Means fornarrowing down the target ‘names-of-classification-items’’’’, and see“§3.3.11.2.3.4.2.3. ‘Means for narrowing down the target ‘descriptors”and ‘Means for narrowing down the target‘names-of-classification-items’’’’), and,

[2101] 3) ‘Means for Fusing propositions’ (For detail, see“§3.3.11.2.3.4.1.4. ‘Means for Fusing propositions’”, and see“§3.3.11.2.3.4.2.4. ‘Means for Fusing propositions’”).

[2102] The lexical definition of

[2103] ‘Means for narrowing down the target ‘descriptors’’,

[2104] the lexical definition of

[2105] ‘Means for narrowing down the target‘names-of-classification-items’’,

[2106] and,

[2107] the lexical definition of

[2108] ‘Means for Fusing propositions’, have already been given in thepresent invention.

Lexical Definition of “Means for Making More Exhaustive Retrieval Whentoo Less Number of Rules-for-Reasoning are Retrieves”

[2109] A measure to prevent the inference from falling into a deadlockafter too less number of ‘rules-for-reasoning’ are retrieved in a stepof opportunistic reasoning (See FIG. 11) is a

[2110] “means for making more exhaustive retrieval when too less numberof rules-for-reasoning are retrieves”.

[2111] “Means for making more exhaustive retrieval when too less numberof ‘rules-for-reasoning’ are retrieves” is a component of each step ofopportunistic reasoning which is carried out only when the inferencefalls into a deadlock, and no step of opportunistic reasoning can not betaken any more (See FIG. 11).

[2112] As recommended procedures with which “means for making moreexhaustive retrieval when too less number of rules-for-reasoning areretrieves” is embodied in an object-oriented knowledge base systemdisclosed in the present invention, there exist three means (See FIG.11):

[2113] 1) ‘means for broadening out the target ‘descriptors’’ (Fordetail, see “§3.3.11.2.3.4.1.5. ‘Means for broadening out the target‘descriptors” and ‘Means for broadening out the target‘names-of-classification-items’’’’, and see “§3.3.11.2.3.4.2.5. ‘Meansfor broadening out the target ‘descriptors” and ‘Means for broadeningout the target ‘names-of-classification-items’’’’) and

[2114] 2) ‘means for broadening out the target‘names-of-classification-items’’ (For detail, see “§3.3.11.2.3.4.1.5.‘Means for broadening out the target ‘descriptors” and ‘Means forbroadening out the target ‘names-of-classification-items’’’’, and see“§3.3.11.2.3.4.2.5. ‘Means for broadening out the target ‘descriptors”and ‘Means for broadening out the target‘names-of-classification-items’’’’).

[2115] The lexical definition of

[2116] ‘Means for narrowing down the target ‘descriptors’’,

[2117] and,

[2118] the lexical definition of

[2119] ‘Means for narrowing down the target‘names-of-classification-items’’,

[2120] have already been given in the present invention.

[2121] At the end of each step of opportunistic reasoning, reasoning iscarried out in which ‘rules-for-reasoning’ is used to try to provelogically the ‘hypothetical proposition that is the target of thepresent step of opportunistic reasoning’.

[2122] If and when this trial has completed and the ‘hypotheticalproposition that is the target of the present step of opportunisticreasoning’ is proved perfectly in the present step of opportunisticreasoning, then, the whole inference is judged to be completed insuccess. Else if this trial has not completed perfectly, next step ofopportunistic reasoning should be carried out to try to complete theinference. In any case, ‘hypothetical propositions which are the targetof the next step of opportunistic reasoning’ should be determined here.If the ‘hypothetical propositions which are the target of the next stepof opportunistic reasoning’ is a trivial proposition, then, the trial isjudged to have been completed and the ‘hypothetical proposition that isthe target of the present step of opportunistic reasoning’ is judged tobe proved perfectly in the present step of opportunistic reasoning.

[2123] The procedure to carry out the process described above is the“means for determining hypothetical propositions which are to be used asthe target of the next step of opportunistic reasoning” (See FIG. 11).The lexical definition of “means for determining hypotheticalpropositions which are to be used as the target of the next step ofopportunistic reasoning” will be given later in the present invention.For detail of “means for determining hypothetical propositions which areto be used as the target of the next step of opportunistic reasoning”,see “§3.3.11.2.3.4.1.7. @[algorithm for determining hypotheticalproposition which is to be used as the target of the next step ofopportunistic reasoning] (Part 1: in the case of forward reasoning)” andsee “§3.3.11.2.3.4.2.7. @[algorithm for determining the hypotheticalproposition which is to be used as the target of the next step ofopportunistic reasoning] (Part 2: in the case of backward reasoning)”.

[2124] Thus far, I have given a rough explanation of components of ‘eachstep of opportunistic reasoning’ by using FIG. 11. Now, I will give someadditional explanation of some features which appear in FIG. 11, step bystep.

3.3.11.2.3.1. Preparation for a “Retrieval of the ‘HypotheticalPropositions Possibly Used in Forward Reasoning”

[2125] <<Lexical Definition of the ‘hypothetical proposition which isthe target of the present step of opportunistic reasoning’ which isdealt with when the ‘present step’ equals to the “n'th execution of theloop body in the ‘while( ){}’ iteration statement‘>> Generally speaking,a ‘hypothetical proposition which is the target of the present step ofopportunistic reasoning’ is a hypothetical proposition which is tried tobe logically proved in the present step of opportunistic reasoning.Here, I define one that is dealt with in the n'th step. At the beginningof the n'th execution of the loop body in the ‘while( ){}’ iterationstatement, we define,

[2126] ‘hypothetical_proposition_(—)

[2127]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2128] of the n'th execution of the loop body in the ‘while( ){}’iteration statement, by an equation,

[2129] ‘hypothetical_proposition_(—)

[2130]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’ ofthe present step of opportunistic reasoning

[2131] =

[2132] one of the

[2133] ‘HypotheticalPropositionsWhichAreToBeUsedAs_(—)

[2134] _TheTargetOfTheNextStepOfOpportunisticRasoning

’, which was determined in the (n−1)'th execution of the loop body inthe ‘while( ){}’ iteration statement in the function in Formula. 2;_,

[2135] In the (n−1)'th execution of the loop body in the ‘while( ){}’iteration statement in the function in Formula. 2, i.e. in the (n−1)'thstep of opportunistic reasoning,

[2136] HypotheticalPropositionsWhichAreToBeUsedAs_(—)

[2137] _TheTargetOfTheNextStepOfOpportunisticRasoning

[2138] are determined by using the function,

[2139] Carry_out_forward_reasoning_and_(—)

[2140] Determine_the_hypothetical_propositions_which_is_to_be_(—)

[2141] the_target_of_the_next_step_of_opportunistic_reasoning(

[2142] HypotheticalPropositionsWhichAreToBeUsedAs_(—)

[2143] _TheTargetOfTheNextStepOfOpportunisticRasoning

;_,

[2144] the detail of which is given in Formula. 3

[2145] and/or

[2146] in the function in Formula. 2,

[2147] Carry_out_backward_reasoning_and_(—)

[2148] Determine_the_hypothetical_propositions_which_is_to_be_the_(—)

[2149] target_of_the_next_step_of_opportunistic_reasoning(

[2150] HypotheticalPropositionsWhichAreToBeUsedAs_(—)

[2151] _TheTargetOfTheNextStepOfOpportunisticRasoning

;_.

[2152] the detail of which is given in Formula. 4.

[2153] If and when more than two ‘Hypothetical propositions that are tobe used as the target of the next step of opportunistic reasoning’exist, then,

[2154] one of them after another should be used as a

[2155] ‘hypothetical_proposition_(—)

[2156]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2157] until all of the ‘Hypothetical propositions that are to be usedas the target of the next step of opportunistic reasoning’ areexhausted, when only one digital computer is used.

[2158] Part of and/or all of the ‘Hypothetical propositions that are tobe used as the target of the next step of opportunistic reasoning’ maybe transmitted as an information to other computers connected with thepresent computer via the internet and/or via a LAN, etc. And each ofthese computers may be used to prove a ‘hypothetical propositions thatare to be used as the target of the next step of opportunisticreasoning’ which the computer received, by using an object-orientedknowledge base system disclosed in the present invention installed onthe computer.

[2159] Of course,

[2160] ‘hypothetical propositions’

[2161]which_are_the_target_of_the_present_step_of_opportunistic_reasoning’

[2162] may be processed by each of the parallel processors of a multiprocessor digital computers.

3.3.11.2.3.1.1. Search of ‘Descriptors’ to be Used for a “Retrieval ofthe ‘Hypothetical Propositions Possibly Used in Forward Reasoning’”3.3.11.2.3.1.1.1. Overview

[2163] In the present execution of the loop body in the ‘while( ){}’iteration statement, first, it is recommended that one should try tosearch ‘descriptors’ which characterize well the ‘presupposition’ of the

[2164] ‘hypothetical_proposition_(—)

[2165]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2166] I call such ‘descriptors’ a usable ‘descriptor’.

[2167] As will be shown later in the present invention, thus introducedusable ‘descriptors’ are a ‘descriptor’ which is to be used to retrievethe “candidates of the ‘rules-for-reasoning’” used in a forwardreasoning.

[2168] This means that forward reasoning is tried to be carried outfirst anyway, here. Backward reasoning is carried out only after it isfound that a forward reasoning is difficult to be carried out. Thisinstruction corresponds to a sentence in the quasi-C function inFormula. 2,

[2169]Search_usable_‘descriptors’_which_represent_suitably_the_‘presupposition’_(—)

[2170] of_the_(—)

[2171] ‘hypothetical_proposition_(—)

[2172]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2173] _by_using_(—)

[2174] @[algorithm of making a list of ‘descriptors’ ranked in order ofhit frequency]_( );_.

[2175] More detailed explanation of this sentence will be given justbelow.

[2176] It is not essential that forward reasoning is tried to be carriedout first. Backward reasoning, instead of forward reasoning, may be ofcourse tried first. But in the present explanation, the case in whichforward reasoning is carried out is given.

[2177] First. a user of the system is recommended to search‘descriptors’ that characterize well the ‘presupposition’ of the

[2178] ‘hypothetical_proposion_(—)

[2179]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2180] by using the object-oriented knowledge base system.

[2181] For a user of the system to make such a search, it is recommendedthat he should use @[algorithm of making a list of ‘descriptors’ rankedin order of hit frequency] on the basis of natural words used in the‘presupposition’. In other words, it is recommended that a user of thesystem to make such a search should

[2182] make a list of the ‘descriptors’ that are associated with

[2183] natural words which characterizes the ‘presupposition’ of the

[2184] ‘hypothetical_proposition_(—)

[2185]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2186] by using

[2187] “means for making a list of ‘descriptors’ ranked in order of hitfrequency” (See FIG. 13).

[2188] And it is recommended that,

[2189] as the basis of data on which

[2190] “means for making a list of ‘descriptors’ ranked in order of hitfrequency”

[2191] is carried out,

[2192] keys described using ‘means for storing data providing theability of association’,

[2193] and/or

[2194] keys described using ‘means for storing the list of lexicalmeanings of a natural word’

[2195] should be mainly used. Of course, other keys in theobject-oriented knowledge base also may be used (See FIG. 13).

[2196] See Formula. 7 for a schematic quasi-C code as an example ofembodiment of @[algorithm of making a list of ‘descriptors’ ranked inorder of hit frequency].

[2197] And if the user of the system finds some usable ‘descriptors’ outof the list, then, it is recommended that the user of the system shouldoptimize such usable ‘descriptors’ by using the object-orientedknowledge base system of the present invention.

[2198] Here, ‘usable’ means to be usable to characterize well thecontents of the ‘presupposition’ of the

[2199] hypothetical_proposition_(—)

[2200]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2201] The way of the optimization depends whether the user of thesystem is willing to make a concentrated “Retrieval of the ‘hypotheticalpropositions to be used in forward reasoning’” and/or willing to make anexhaustive “retrieval of the ‘hypothetical propositions to be used inforward reasoning’”.

[2202] This situation corresponds to a sentence in the quasi-C functionin Formula. 2, if(some usable ‘descriptors’ are retrieved) {if(concentrated retrieval is to be carried out) {Optimize_usable_‘descriptors’_for_concentrated_(—) “retrieval of the‘hypothetical propositions possibly used in forwardreasoning’”_by_using_‘ideal_thesaurus’( ) ;_(—)confirming_the_optimized_‘descriptors’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) } else if(exhaustive retrieval is to be carried out) {Optimize_usable_‘descriptors’_for_exhaustive_(—) “retrieval of the‘hypothetical propositions to be used in forward reasoning’”_(—)by_using_‘ideal_thesaurus’( ) ;_(—)confirming_the_optimized_‘descriptors’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—)

[2203] Here, I give a lexical definition of “means for getting‘descriptors’ that are used to make a query to get the “candidates ofthe ‘rules-for-reasoning’””.

[2204] It should be noted that how a

[2205] “means for getting ‘descriptors’ that are used to make a query toget the “candidates of

[2206] the ‘rules-for-reasoning’””

[2207] is used in

[2208] each step of opportunistic reasoning

[2209] has already explained in “§3.3.11.2.3.0. Overview”.

Lexical Definition of “Means for Getting ‘Descriptors’ that are Used toMake a Query to get the “Candidates of the ‘Rules-for-Reasoning’””(Part 1. In the Case of Forward Reasoning

[2210] The procedure described by the sentence just mentioned above, inthe quasi-C function in Formula. 2,Search_usable_‘descriptors’_which_represent_suitably_the_‘presupposition’_(—)of_the_(—) ‘hypothetical_proposition_(—)which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_(—)_by_using_(—) @[algorithm of making a list of ‘descriptors’ ranked inorder of hit frequency]_( ) ;_(—) if(some usable ‘descriptors’ areretrieved) { if(concentrated retrieval is to be carried out) {Optimize_usable_‘descriptors’_for_concentrated_(—) “retrieval of the‘hypothetical propositions possibly used in forwardreasoning’”_by_using_‘ideal_thesaurus’( ) ;_(—)confirming_the_optimized_‘descriptors’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) } else if(exhaustive retrieval is to be carried out) {Optimize_usable_‘descriptors’_for_exhaustive_(—) “retrieval of the‘hypothetical propositions to be used in forward reasoning’”_(—)by_using_‘ideal_thesaurus’( ) ;_(—)confirming_the_optimized_‘descriptors’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—)

[2211] is a recommended “means for getting ‘descriptors’ that are to beused to make a query to get the “candidates of the‘rules-for-reasoning’””, in the case of forward reasoning.

[2212] The lexical definition of ‘descriptors’ that are used to make aquery to get the “candidates of the ‘rules-for-reasoning’” (part 1. Inthe case of forward reasoning), will be given later in the presentinvention.

[2213] The recommended constitution how “means for getting ‘descriptors’that are used to make a query to get the “candidates of the‘rules-for-reasoning’”” is embodied, is schematically shown in FIG. 13.

3.3.11.2.3.1.1.2. Search of ‘Descriptors’ to be Used for a Concentrated“Retrieval of the ‘Hypothetical Propositions Possibly Used in ForwardReasoning’”

[2214] First, I give an explanation about the sentence in Formula. 2,which I have shown just above, if(some usable ‘descriptors’ areretrieved) { if(concentrated retrieval is to be carried out) {Optimize_usable_‘descriptors’_for_concentrated_(—) “retrieval of the‘hypothetical propositions possibly used in forwardreasoning’”_by_using_‘ideal_thesaurus’( ) ;_.

[2215] That is, if the user of the system is willing to make aconcentrated “retrieval of the ‘hypothetical propositions to be used inforward reasoning’”, then, it is recommended that he should search asnarrower ‘descriptors’ as possible that represents the most preciselyand specifically the ‘subject-word (S)’, ‘object-word (O)’,‘indirect-object-word (I.O)’, and/or, ‘direct-object-word (D.O)’ of the‘presupposition’ of the

[2216] ‘hypothetical_proposition_(—)

[2217] which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2218] and that he should search as narrower ‘descriptors’ as possiblethat represents the ‘complement-word (C)’ of the ‘presupposition’ of the

[2219] ‘hypothetical_proposition_(—)

[2220]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2221] In ideal cases when the maker of the contents has alreadyassigned the most specific ‘descriptors’ to the ‘presupposition’, theuser of the system, of course, need not have to come again after themaker of the system.

[2222] For such optimizations, it is recommended that the user of thesystem should use ‘ideal thesaurus’; That is, if the user of the systemis wiling to make a concentrated “retrieval of the ‘hypotheticalpropositions to be used in forward reasoning’”, then, it is recommendedthat he should search ‘descriptors’ as narrow and/or broader as possiblewhich are not only usable but also narrower than the usable‘descriptors’, in the ‘ideal thesaurus’. In this case, it is recommendedthat he should use a computer flexibly to search and list ‘descriptors’that are narrower and/or broader than the usable ‘descriptors’. Aquasi-C code which outlines the procedure to search and list the‘descriptors’ that are narrower (by one rank) than a ‘descriptor’ isshown in Formula. 13. It is recommended that the user of the systemshould use flexibly the procedure which is outlined as a quasi-C code inFormula. 13.

[2223] As an example of such a flexible use, the procedure which isoutlined as a quasi-C code in Formula. 13 may be used recursively andcompletely, by using the usable ‘descriptors’ as the original seeds.That is, one may regard all the usable ‘descriptors’ included in thelist of the ‘descriptors’ that are narrower (by one rank) than a seed‘descriptor’, as another seed ‘descriptor’. If this procedure iscontinued until no usable narrower ‘descriptor’ can be listed any more,then, as the result, all the usable ‘descriptors’ that are narrower thanthe usable ‘descriptors’ can be obtained.

[2224] A quasi-C code which outlines the procedure to search and listthe ‘descriptors’ that are broader (by one rank) than a ‘descriptor’ isshown in Formula. 10. It is recommended that the user of the systemshould use flexibly the procedure which is outlined as a quasi-C code inFormula. 10.

[2225] As an example of such a flexible use, the procedure which isoutlined as a quasi-C code in Formula. 10 may be used recursively andcompletely, by using the usable ‘descriptors’ as the original seeds.That is, one may regard all the usable ‘descriptors’ included in thelist of the ‘descriptors’ that are broader (by one rank) than a seed‘descriptor’, as another seed ‘descriptor’. If this procedure iscontinued until no usable broader ‘descriptor’ can not be listed anymore, then, as the result, all the usable ‘descriptors’ that are broaderthan the usable ‘descriptors’ can be obtained.

[2226] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in forward reasoning’”, it isrecommended that he should confirm the usableness

[2227] (i.e. to be usable to characterize well the contents of the‘presupposition’ of the ‘hypothetical_proposition_(—)

[2228]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’),

[2229] of the ‘descriptors’ thus obtained by watching the keys and therecords that are associated with the ‘descriptors’. If the user of thesystem contrives his original ‘hypothetical proposition’ in this stageor other, then, of course, he may provide his original ‘hypotheticalproposition’ as a knowledge to be used for the system's inference duringhis own operation. It is recommended that a knowledge engineer shouldjudge such a ‘hypothetical proposition’ whether to be of universal useand/or to be only of special use.

[2230] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above,confirming_the_optimized_‘descriptors’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) }.

3.3.11.2.3.1.1.3. Search of ‘Descriptors’ to be used for a Exhaustive“Retrieval of the ‘Hypothetical Propositions Possibly Used in ForwardReasoning’”

[2231] Second, I give an explanation about the sentence in Formula. 2,which I have shown just above, else if(exhaustive retrieval is to becarried out) { Optimize_usable_‘descriptors’_for_exhaustive_(—)“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’”_(—) by_using_‘ideal thesaurus’( ) ;_.

[2232] That is, if the user of the system is willing to make aexhaustive “retrieval of the ‘hypothetical propositions to be used inforward reasoning’”, then, it is recommended that he should search asbroader ‘descriptors’ as possible that represent most universally the‘subject-word (S)’, ‘object-word (O)’, ‘indirect-object-word (I.O)’,and/or ‘direct-object-word (D.O)’ of the ‘presupposition’ of the

[2233] ‘hypothetical_proposition_(—)

[2234]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2235] and that he should search as narrower ‘descriptors’ as possiblethat represent most universally the ‘complement-word (C)’ of the‘presupposition’ of the

[2236] ‘hypothetical_proposition_(—)

[2237]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2238] For such optimizations, it is recommended that the user of thesystem should use ‘ideal thesaurus’; That is, if the user of the systemis wiling to make an exhaustive “retrieval of the ‘hypotheticalpropositions to be used in forward reasoning’”, then, it is recommendedthat he should search as broad and/or narrow as possible ‘descriptors’of the usable ‘descriptors’, from the ‘ideal thesaurus’. In this case,it is recommended that he should use a computer flexibly to search andlist ‘descriptors’ that are narrower and/or broader than the usable‘descriptors’. The way in which a computer is used flexibly to searchand list ‘descriptors’ that are narrower and/or broader than the usable‘descriptors’ has already described in the section of“§3.3.11.2.3.1.1.2.Search of ‘Descriptors’ to be used for a Concentrated “Retrieval of the‘Hypothetical propositions possibly used in Forward Reasoning’””.

[2239] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in forward reasoning’”, it isrecommended that he should confirm the suitableness of the ‘descriptors’thus obtained by watching the keys and the records that are associatedwith the ‘descriptors’. If the user of the system contrives his original‘hypothetical proposition’ in this stage, then of course, he may providehis original ‘hypothetical proposition’ as a knowledge to be used forthe system's inference during his own operation. It is recommended inthis case, too, that a knowledge engineer should judge such a‘hypothetical proposition’ whether to be of universal use and/or to beonly of special use.

[2240] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above,confirming_the_optimized_‘descriptors’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) } }.

[2241] In some case in which no appropriate ‘descriptors’ are found, itis recommended that the user of the system should find and use‘next-best-natural-nouns’ instead of ‘descriptors’ in the “retrieval ofthe ‘hypothetical propositions to be used in forward reasoning’” of thepresent execution of the loop body in the ‘while( ){}’ iterationstatement.

3.3.11.2.3.1.2. Search of ‘Names-of-Classification-Items’ to be Used fora “Retrieval of the ‘Hypothetical Propositions Possibly Used in ForwardReasoning’” 3.3.11.2.3.1.2.1. Overview

[2242] In the present execution of the loop body in the ‘while( ){}’iteration statement, second, it is recommended that one should try tosearch ‘names-of-classification-items’ which characterize well the‘presupposition’ of the

[2243] ‘hypothetical_proposition_(—)

[2244]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2245] As will be shown later in the present invention, thus introducedusable ‘names-of-classification-items’ are a‘name-of-classification-item’ which is to be used in the case of forwardreasoning to retrieve the “candidates of the ‘rules-for-reasoning’”. Asmentioned before, this means that forward reasoning is tried to becarried out first anyway. Backward reasoning is carried out only afterit is found that a forward reasoning is difficult to be carried out.

[2246] This instruction corresponds to a sentence in the quasi-Cfunction in Formula. 2,

[2247] Search_usable_‘names-of-classification-items’_(—)

[2248] which_represent_suitably_the_(—)

[2249] ‘presupposition’_(—)

[2250] of_the_(—)

[2251] ‘hypothetical_proposition_(—)

[2252]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_(—)

[2253] by_using_(—)

[2254] @[algorithm of looking through ‘names-of-classification-items’ inthe order of hit frequency]_( );_(—)

[2255] More detailed explanation of this function will be given justbelow. First. a user of the system is recommended to search‘names-of-classification-items’ that characterize well the‘presupposition’ of the

[2256] hypothetical_proposition_(—)

[2257]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2258] by using the object-oriented knowledge base system.

[2259] For the user of the system to make such a search, it isrecommended that he should use @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] on thebasis of the natural words in the ‘presupposition’. In other words, itis recommended that a user of the system to make such a search shouldmake a list of the ‘names-of-classification-items’ that are associatedwith

[2260] natural words which characterizes the ‘presupposition’ of the

[2261] ‘hypothetical_proposition_(—)

[2262]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2263] by using

[2264] “means for making a list of ‘names-of-classification-items’ranked in order of hit

[2265] frequency”

[2266] (See FIG. 14).

[2267] And it is recommended that,

[2268] as the basis of data on which

[2269] “means for making a list of ‘names-of-classification-items’ranked in order of hit

[2270] frequency”

[2271] is carried out,

[2272] keys described using ‘means for storing data providing theability of association’,

[2273] and/or

[2274] keys described using ‘means for storing the list of lexicalmeanings of a natural word’ should be mainly used. Of course, other keysin the object-oriented knowledge base also may be used (See FIG. 14).

[2275] See Formula. 8A and Formula. 8B for a schematic quasi-C code asan example of embodiment of @[algorithm of making a list of‘descriptors’ ranked in order of hit frequency].

[2276] And if the user of the system finds some usable‘names-of-classification-items’ out of the list, then, it is recommendedthat the user of the system should optimize such usable‘names-of-classification-items’ by using the object-oriented knowledgebase system of the present invention. The way of the optimizationdepends whether the user of the system is willing to make a concentrated“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’” and/or wiling to make an exhaustive “retrieval of the‘hypothetical propositions to be used in forward reasoning’”. Thissituation corresponds to a sentence in the quasi-C function in Formula.2, if(some usable ‘names-of-classification-items’ are retrieved) {if(concentrated retrieval is to be carried out) {Optimize_usable_‘names-of-classification-items’_for_concentrated_(—)“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’”_by_using_‘classification_table’_( ) ;_(—)confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) } else if(exhaustive retrieval is to be carried out) {Optimize_usable_‘names-of-classification-items’_for_exhaustive_(—)“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’”_by_using_‘classification_table’( ) ;_(—)confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) } }

Lexical Definition of “Means for Getting ‘Names-of-Classification-Items’that are Used to Make a Query to get the “Candidates of the‘Rules-for-Reasoning’” (Part 1. In the Case of Forward Reasoning

[2277] The procedure described by the sentence just mentioned above, inthe quasi-C function in Formula. 2,Search_usable_‘names-of-classification-items’_(—)which_represent_suitably_the_(—) ‘presupposition’_(—) of_the_(—)‘hypothetical_proposition_(—)which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_(—)by_using_(—) @[algorithm of looking through‘names-of-classification-items’ in the order of hit frequency]_( ) ;_(—)if(some usable ‘names-of-classification-items’ are retrieved) {if(concentrated retrieval is to be carried out) {Optimize_usable_‘names-of-classification-items’_for_concentrated_(—)“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’”_by_using_‘classification_table’_( ) ;_(—)confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) } else if(exhaustive retrieval is to be carried out) {Optimize_usable_‘names-of-classification-items’_for_exhaustive_(—)“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’”_by_using_‘classification_table’( ) ;_(—)confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( );_(—) } }

[2278] is a recommended “means for getting‘names-of-classification-items’ that are used to make a query to get the“candidates of the ‘rules-for-reasoning’””, in the case of forwardreasoning.

[2279] The lexical definition of ‘names-of-classification-items’ thatare used to make a query to get the “candidates of the‘rules-for-reasoning’” (part 1. In the case of forward reasoning), willbe given later in the present invention.

[2280] The recommended constitution how “means for getting‘names-of-classification-items’ that are used to make a query to get the“candidates of the ‘rules-for-reasoning’”” is embodied, is schematicallyshown in FIG. 14.

3.3.11.2.3.1.2.2. Search of ‘Names-of-Classification-Items’ to be Usedfor a Concentrated “Retrieval of the ‘Hypothetical Propositions PossiblyUsed in Forward Reasoning’”

[2281] First I give an explanation about the sentence in Formula. 2,which I have shown just above, if(some usable‘names-of-classification-items’ are retrieved) { if(concentratedretrieval is to be carried out) {Optimize_usable_‘names-of-classification-items’_ for_concentrated_(—)“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’”_by_using_‘classification_table’_( ) ;_(—)

[2282] That is, if the user of the system is willing to make aconcentrated “retrieval of the ‘hypothetical propositions to be used inforward reasoning’”, then, it is recommended that he should search lowerclass ‘names-of-classification-items’ that represents the most preciselyand specifically the contents of the ‘presupposition’ of the

[2283] ‘hypothetical_proposition_(—)

[2284] which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2285] In ideal cases when the maker of the contents has alreadyassigned the most specific ‘names-of-classification-items’ to the‘presupposition’, the user of the system, of course, need not have tocome again after the maker of the system.

[2286] For such optimizations, it is recommended that the user of thesystem should use ‘classification table’; That is, if the user of thesystem is wiling to make a concentrated “retrieval of the ‘hypotheticalpropositions to be used in forward reasoning’”, it is recommended thathe should search the ‘names-of-classification-items’ as low class aspossible which are not only usable but also lower class of the usable‘names-of-classification-items’ from the ‘classification table’. In thiscase, it is recommended that he should use a computer flexibly to searchand list ‘names-of-classification-items’ that are lower class of theusable ‘names-of-classification-items’. A quasi-C code which outlinesthe procedure to search and list the ‘names-of-classification-items’that are lower class (by one rank) of a ‘name-of-classification-item’ isshown in Formula. 14. It is recommended that the user of the systemshould use flexibly the procedure which is outlined as a quasi-C code inFormula. 14.

[2287] As an example of such a flexible use, the procedure which isoutlined as a quasi-C code in Formula. 14 may be used recursively andcompletely, by using the usable ‘names-of-classification-items’ as theoriginal seeds. That is, one may regard all the usable‘names-of-classification-items’ included in the list of the‘names-of-classification-items’ that are lower class (by one rank) of aseed ‘name-of-classification-item’, as another seed‘name-of-classification-item’. If this procedure is continued until nousable lower class ‘name-of-classification-items’ can be listed anymore, then, as the result, all the usable ‘name-of-classification-items’that are lower class of the usable ‘name-of-classification-items’ can beobtained.

[2288] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in forward reasoning’”, then,it is recommended that he should confirm the suitableness

[2289] (i.e. to be suitable to represent the contents of the‘presupposition’ of the ‘hypothetical_proposition_(—)

[2290]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’)

[2291] of the ‘name-of-classification-items’ thus obtained by watchingthe keys and the records that are associated with the‘name-of-classification-items’. If the user of the system contrives hisoriginal ‘hypothetical proposition’ in this stage or other, then, ofcourse, he may provide his original ‘hypothetical proposition’ as aknowledge to be used for the system's inference during his ownoperation. It is recommended in this case, too, that a knowledgeengineer should judge such a ‘hypothetical proposition’ whether to be ofuniversal use and/or to be only of special use.

[2292] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above,confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) }.

3.3.11.2.3.1.2.3. Search of ‘Names-of-Classification-Items’ to be Usedfor a Exhaustive “Retrieval of the ‘Hypothetical Propositions PossiblyUsed in Forward Reasoning’”

[2293] Second, I give an explanation about the sentence in Formula. 2,which I have shown just above, else if(exhaustive retrieval is to becarried out) {Optimize_usable_‘names-of-classification-items’_for_exhaustive_(—)“retrieval of the ‘hypothetical propositions to be used in forwardreasoning’”_by_using_‘classification_table’( ) ;_(—)

[2294] That is, if the user of the system is willing to make aexhaustive “retrieval of the ‘hypothetical propositions to be used inforward reasoning’”, then, it is recommended that he should searchhigher class of ‘names-of-classification-items’ that represent mostuniversally the contents of the ‘presupposition’ of the

[2295] ‘hypothetical_proposition_(—)

[2296]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2297] For such optimizations, it is recommended that the user of thesystem should use ‘classification table’; That is, if the user of thesystem is wiling to make an exhaustive “retrieval of the ‘hypotheticalpropositions to be used in forward reasoning’”, then, it is recommendedthat he should search ‘names-of-classification-items’ as high class aspossible which are not only usable but also higher class of the usable‘names-of-classification-items’, from the ‘classification table’. Inthis case, it is recommended that he should use a computer to search andlist the ‘names-of-classification-items’ that are higher class of theusable ‘name-of-classification-item’. A quasi-C code which outlines theprocedure to search and list the ‘names-of-classification-items’ thatare higher class (by one rank) of a ‘name-of-classification-item’ isshown in Formula. 11. It is recommended that the user of the systemshould use flexibly the procedure which is outlined as a quasi-C code inFormula. 11.

[2298] As an example of such a flexible use, the procedure which isoutlined as a quasi-C code in Formula. 11 may be used recursively andcompletely, by using the usable ‘names-of-classification-items’ as theoriginal seeds. That is, one may regard all the usable‘names-of-classification-items’ included in the list of the‘names-of-classification-items’ that are higher class (by one rank) of aseed ‘name-of-classification-item’, as another seed‘name-of-classification-item’. If this procedure is continued until nousable higher class ‘names-of-classification-items’ can not be listedany more, then, as the result, all the usable‘names-of-classification-items’ that are higher class of the usable‘names-of-classification-items’ can be obtained.

[2299] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in forward reasoning’”, it isrecommended that he should confirm the usableness

[2300] (i.e. to be usable to characterize well the contents of the‘presupposition’ of the ‘hypothetical_proposition_(—)

[2301]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’),

[2302] of the ‘names-of-classification-items’ thus obtained by watchingthe keys and the records that are associated with the‘names-of-classification-items’. If the user of the system contrives hisoriginal ‘hypothetical proposition’ in this stage, then of course, hemay provide his original ‘hypothetical proposition’ as a knowledge to beused for the system's inference during his own operation. It isrecommended in this case, too, that a knowledge engineer should judgesuch a ‘hypothetical proposition’ whether to be of universal use and/orto be only of special use.

[2303] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above,confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—) } }

[2304] In some case in which no appropriate‘names-of-classification-items’ are found, it is recommended that theuser of the system should find and use ‘next-best-natural-verbs’ insteadof ‘names-of-classification-items’ in the “retrieval of the‘hypothetical propositions to be used in forward reasoning’” of thepresent execution of the loop body in the ‘while( ){}’ iterationstatement.

[2305] If ‘descriptors’ and/or ‘algorithm-of-process’ characterizingwell the ‘presupposition’ of the

[2306] ‘hypothetical_proposition_(—)

[2307]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2308] are found,

[2309] and/or

[2310] a user of the system contrives ‘next-best-natural-nouns’ and/or‘next-best-natural-verbs’ characterizing well the ‘presupposition’ ofthe ‘hypothetical_proposition_(—)

[2311]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2312] then,

[2313] it is recommended that forward reasoning should be carried out inthe present step of opportunistic reasoning (i.e. in the presentexecution of the loop body in the ‘while( ){}’ iteration statement).

[2314] If and when the ‘presupposition’ of the hypothetical propositionis an empty set, forward reasoning can not be carried out because‘descriptors’ and/or ‘algorithm-of-process’ describing ‘presupposition’.

[2315] The situation described here above is shown in Formula. 2 as,if(‘descriptors’ and/or ‘names-of-classification-items’ describing the‘presupposition’ of the ‘hypothetical_proposition_(—)which_is_the_target_of_the _present_step_of_opportunistic_(—) reasoning’are found and/or a user of the system contrives a good‘next-best-natural-nouns’ and/or a good ‘next-best-natural-verbs’) { Carry_out_forward_reasoning_and_(—) Determine_the_hypothetical_propositions_which_is_to_be_(—) the_target_of_the_next_step_of_opportunistic_reasoning( ) ;_(—) }

[2316] The recommended way how a forward reasoning should be carried outin the present step of opportunistic reasoning is shown in Formula. 3.Detailed descriptions will be given later in the present invention.

3.3.11.2.3.2. Preparation for a “Retrieval of the ‘HypotheticalPropositions Possibly Used in Backward Reasoning’”

[2317] In the case,

[2318] no ‘descriptors’ and/or ‘names-of-classification-items’describing the

[2319] ‘presupposition’ of the

[2320] ‘hypothetical_proposition_(—)

[2321]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2322] is found,

[2323] and

[2324] a user of the system can not contrive a good‘next-best-natural-nouns’ and/or

[2325] a good ‘next-best-natural-verbs’,

[2326] it is recommended that preparation should made for a “retrievalof the ‘hypothetical propositions to be used in backward reasoning’”

[2327] This instruction corresponds to a sentence in the quasi-Cfunction in Formula. 2,

[2328] else

[2329] {.

3.3.11.2.3.2.1. Search of ‘Descriptors’ to be Used for a “Retrieval ofthe ‘Hypothetical Propositions Possibly Used in Backward Reasoning’”3.3.11.2.3.2.1.1. Overview

[2330] As the preparation for a “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’”, first, it isrecommended that ‘descriptors’ which characterize well the ‘consequence’of the

[2331] ‘hypothetical_proposition_(—)

[2332]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2333] should be searched. As will be shown later in the presentinvention, thus introduced usable ‘descriptors’ are a ‘descriptor’ whichis to be used in the case of backward reasoning to retrieve the“candidates of the ‘rules-for-reasoning’”. This instruction correspondsto a sentence in the quasi-C function in Formula. 2,

[2334]Search_usable_‘descriptors’_which_represent_suitably_the_‘consequence’_(—)

[2335] of_the_(—)

[2336] ‘hypothetical_proposition_(—)

[2337]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_(—)

[2338] _by_using_(—)

[2339] @[algorithm of making a list of ‘descriptors’ ranked in order ofhit frequency]( );_.

[2340] More detailed explanation of this sentence will be given justbelow.

[2341] First. a user of the system is recommended to search‘descriptors’ that characterize well the ‘consequence’ of the

[2342] ‘hypothetical_proposition_(—)

[2343]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2344] by using the object-orient knowledge base system.

[2345] For a user of the system to make such a search, it is recommendedthat he should use @[algorithm of making a list of ‘descriptors’ rankedin order of hit frequency] on the basis of natural words used in the‘consequence’. In other words, it is recommended that a user of thesystem to make such a search should

[2346] make a list of the ‘descriptors’ that are associated with

[2347] natural words which characterizes the ‘consequence’ of the

[2348] ‘hypothetical_proposition_(—)

[2349]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2350] by using

[2351] “means for making a list of ‘descriptors’ ranked in order of hitfrequency”

[2352] (See FIG. 13).

[2353] And it is recommended that,

[2354] as the basis of data on which

[2355] “means for making a list of ‘descriptors’ ranked in order of hitfrequency”

[2356] is carried out,

[2357] keys described using ‘means for storing data providing theability of association’,

[2358] and/or

[2359] keys described using ‘means for storing the list of lexicalmeanings of a natural word’ should be mainly used. Of course, other keysin the object-oriented knowledge base also may be used (See FIG. 13).

[2360] See Formula. 7 for a schematic quasi-C code as an example ofembodiment of @[algorithm of making a list of ‘descriptors’ ranked inorder of hit frequency].

[2361] And if the user of the system finds some usable ‘descriptors’ outof the list, then, it is recommended that the user of the system shouldoptimize such usable ‘descriptors’.

[2362] Here, ‘usable’ means to be usable to characterize well thecontents of the ‘consequence’ of the

[2363] ‘hypothetical_proposition_(—)

[2364]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2365] The way of the optimization depends whether the user of thesystem is willing to make a concentrated “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’” and/or willing to makean exhaustive “retrieval of the ‘hypothetical propositions to be used inbackward reasoning’”. This situation corresponds to a sentence in thequasi-C function in Formula. 2, if(some usable ‘descriptors’ areretrieved) {  if(concentrated retrieval is to be carried out)  {  Optimize_usable_‘descriptors’_for_concentrated_(—)   “retrieval of the‘hypothetical propositions possibly used in backward  reasoning’”_by_using_‘ideal_thesaurus’( ) ;_(—)  confirming_the_optimized_‘descriptors’_(—)  by_using_keys_and_records_of_the_corresponding_data _(—)    if_necessary ( )  ; _(—)    }    else if(exhaustive retrieval is tobe carried out)    {    Optimize_usable_‘descriptors’_for_exhaustive_(—)     “retrieval ofthe ‘hypothetical propositions     to be used in backward    reasoning’”_(—)     by_using_‘ideal_thesaurus’( ) ;_(—)  confirming_the_optimized_‘descriptors’_(—)  by_using_keys_and_records_of_the_corresponding_data_if_(—)   necessary( ) ; _(—)  } }.

[2366] Here, I give a lexical definition of “means for getting‘descriptors’ that are used to make a query to get the “candidates ofthe ‘rules-for-reasoning’””.

[2367] It should be noted that how a

[2368] “means for getting ‘descriptors’ that are used to make a query toget the “candidates of the ‘rules-for-reasoning’””

[2369] is used in

[2370] each step of opportunistic reasoning

[2371] has already explained in “§3.3.11.2.3.0. Overview”.

Lexical Definition of “Means for Getting ‘Descriptors’ that are Used toMake a Query to get the “Candidates of the ‘Rules-for-Reasoning’”” (Part2. In the Case of Backward Reasoning

[2372] The procedure described by the sentence just mentioned above, inthe quasi-C function in Formula. 2,Search_usable_‘descriptors’_which_represent_suitably_the_‘consequence’_(—)  of_the_(—)   ‘hypothetical_proposition_(—)  which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_(—)  _by_using_(—)   @[algorithm of making a list of ‘descriptors’ rankedin order of hit   frequency]_( ) ;_.   if(some usable ‘descriptors’ areretrieved)   {    if(concentrated retrieval is to be carried out)    {    Optimize_usable_‘descriptors’_for_concentrated_(—)     “retrieval ofthe ‘hypothetical propositions possibly used in backward    reasoning’”_by_using_‘ideal_thesaurus’( ) ;_(—)    confirming_the_optimized_‘descriptors’_(—)    by_using_keys_and_records_of_the_corresponding_data_(—)    if_necessary ( ) ; _(—)    }    else if(exhaustive retrieval is tobe carried out)    {    Optimize_usable_‘descriptors’_for_exhaustive_(—)     “retrieval ofthe ‘hypothetical propositions to be used in backward    reasoning’”_(—)     by_using_‘ideal_thesaurus’( ) ;_(—)  confirming_the_optimized_‘descriptors’_(—)  by_using_keys_and_records_of_the_corresponding_data_if_necessary ( ) ;_(—)  } }.

[2373] is a recommended “means for getting ‘descriptors’ that are usedto make a query to get the “candidates of the ‘rules-for-reasoning’””,in the case of backward reasoning.

[2374] The lexical definition of ‘descriptors’ that are used to make aquery to get the “candidates of the ‘rules-for-reasoning’” (part 2. Inthe case of backward reasoning), will be given later in the presentinvention.

[2375] The recommended constitution how “means for getting ‘descriptors’that are used to make a query to get the “candidates of the‘rules-for-reasoning’”” is embodied, is schematically shown in FIG. 13.

[2376] 3.3.11.2.3.2.1.2. Search of ‘Descriptors’ to be Used for aConcentrated “Retrieval of the ‘Hypothetical Propositions Possibly Usedin Backward Reasoning’”

[2377] First I give an explanation about the sentence in Formula. 2,which I have shown just above, if(some usable ‘descriptors’ areretrieved) {  if(concentrated retrieval is to be carried out)  {  Optimize_usable_‘descriptors’_for_concentrated_(—)   “retrieval of the‘hypothetical propositions possibly used in backward  reasoning’”_by_using_‘ideal_thesaurus’( ) ;_(—)

[2378] That is, if the user of the system is willing to make aconcentrated “retrieval of the ‘hypothetical propositions to be used inbackward reasoning’”, then, it is recommended that he should search asnarrower ‘descriptors’ as possible that represents the most preciselyand specifically the ‘subject-word (S)’, ‘object-word (O)’,‘indirect-object-word (I.O)’, and/or, ‘direct-object-word (D.O)’ of the‘consequence’ of the

[2379] ‘hypothetical_proposition_(—)

[2380]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2381] and that he should search as narrower ‘descriptors’ as possiblethat represents the ‘complement-word (C)’ of the ‘consequence’ of the

[2382] ‘hypothetical_proposition_(—)

[2383]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2384] In ideal cases when the maker of the contents has alreadyassigned the most specific ‘descriptors’ to the ‘consequence’, the userof the system, of course, need not have to come again after the maker ofthe system.

[2385] For such optimizations, it is recommended that the user of thesystem should use ‘ideal thesaurus’; That is, if the user of the systemis wiling to make a concentrated “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’”, then, it is recommendedthat he should search a ‘descriptors’ as narrow and/or broad as possiblewhich are not only usable but also narrower than the usable‘descriptors’, from the ‘ideal thesaurus’. In this case, it isrecommended that he should use a computer flexibly to search and list‘descriptors’ that are narrower than the usable ‘descriptors’. The wayin which a computer is used flexibly to search and list ‘descriptors’that are narrower and/or broader than the usable ‘descriptors’ hasalready described in the section of “§3.3.11.2.3.1.1.2. Search of‘Descriptors’ to be used for a Concentrated “Retrieval of the‘Hypothetical propositions possibly used in Forward Reasoning’””.

[2386] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in backward reasoning’”, it isrecommended that he should confirm the usableness

[2387] (i.e. to be usable to characterize well the contents of the‘conclusion’ of the ‘hypothetical_proposition_(—)

[2388]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’),

[2389] of the ‘descriptors’ thus obtained by watching the keys and therecords that are associated with the ‘descriptors’.

[2390] If the user of the system contrives his original ‘hypotheticalproposition’ in this stage or other, then, of course, he may provide hisoriginal ‘hypothetical proposition’ as a knowledge to be used for thesystem's inference during his own operation. It is recommended in thiscase, too, that a knowledge engineer should judge such a ‘hypotheticalproposition’ whether to be of universal use and/or to be only of specialuse.

[2391] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above, confirming_the_optimized_‘descriptors’_(—) by_using_keys_and_records_of_the_corresponding_data_(—)  if_necessary () ; _(—) }.

3.3.11.2.3.2.1.3. Search of ‘Descriptors’ to be Used for a Exhaustive“Retrieval of the ‘Hypothetical Propositions Possibly Used in BackwardReasoning’”

[2392] Second, I give an explanation about the sentence in Formula. 2,which I have shown just above, else if(exhaustive retrieval is to becarried out) {  Optimize_usable_‘descriptors’_for_exhaustive_(—) “retrieval of the ‘hypothetical propositions to be used in backward reasoning’”_(—)  by_using_‘ideal_thesaurus’( ) ;_(—)

[2393] That is, if the user of the system is willing to make aexhaustive “retrieval of the ‘hypothetical propositions to be used inbackward reasoning’”, then, it is recommended that he should search asbroader ‘descriptors’ as possible that represent most universally the‘subject-word (S)’, ‘object-word (O)’, ‘indirect-object-word (I.O)’,and/or ‘direct-object-word (D.O)’ of the ‘consequence’ of the

[2394] ‘hypothetical_proposition_(—)

[2395]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2396] and that he should search as narrower ‘descriptors’ as possiblethat represent most universally the ‘complement-word (C)’ of the‘consequence’ of the

[2397] ‘hypothetical_proposition_(—)

[2398]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2399] For such optimizations, it is recommended that the user of thesystem should use ‘ideal thesaurus’; That is, if the user of the systemis wiling to make an exhaustive “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’”, then, it is recommendedthat he should search as broad and/or narrow as possible ‘descriptors’of the usable ‘descriptors’, from the ‘ideal thesaurus’. In this case,it is recommended that he should use a computer flexibly to search andlist ‘descriptors’ that are narrower and/or broader than the usable‘descriptors’. The way in which a computer is used flexibly to searchand list ‘descriptors’ that are narrower and/or broader than the usable‘descriptors’ has already described in the section of“§3.3.11.2.3.1.1.2. Search of ‘Descriptors’ to be used for aConcentrated “Retrieval of the ‘Hypothetical propositions possibly usedin Forward Reasoning’””.

[2400] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in backward reasoning’”, it isrecommended that he should confirm the usableness

[2401] (i.e. to be usable to characterize well the contents of the‘conclusion’ of the ‘hypothetical_proposition_(—)

[2402]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’),

[2403] of the ‘descriptors’ thus obtained by watching the keys and therecords that are associated with the ‘descriptors’. If the user of thesystem contrives his original ‘hypothetical proposition’ in this stage,then of course, he may provide his original ‘hypothetical proposition’as a knowledge to be used for the system's inference during his ownoperation. It is recommended that a knowledge engineer should judge sucha ‘hypothetical proposition’ whether to be of universal use and/or to beonly of special use.

[2404] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above,  confirming_the_optimized_‘descriptors’_(—)  by_using_keys_and_records_of_the_corresponding_data_(—)   if_necessary( ) ; _(—)  } }.

3.3.11.2.3.2.2. Search of ‘Names-of-Classification-Items’ to be Used fora “Retrieval of the ‘Hypothetical Propositions Possibly Used in BackwardReasoning’” 3.3.11.2.3.2.2.1. Overview

[2405] As the preparation for a “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’”, second, it isrecommended that ‘names-of-classification-items’ which characterize wellthe ‘consequence’ of the

[2406] ‘hypothetical_proposition_(—)

[2407]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2408] should be searched. As will be shown later in the presentinvention, thus introduced ‘names-of-classification-items’ are a‘name-of-classification-item’ which is to be used in the case ofbackward reasoning to retrieve the “candidates of the‘rules-for-reasoning’”. This instruction corresponds to a sentence inthe quasi-C function in Formula. 2,

[2409] Search_usable_‘names-of-classification-items’_(—)

[2410] which_represent_suitably_the_(—)

[2411] ‘consequence’_(—)

[2412] of_the_(—)

[2413] ‘hypothetical_proposition_(—)

[2414]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_(—)

[2415] by_using_@[algorithm of looking through‘names-of-classification-items’ in the order of hit frequency]_( );_(—)

[2416] More detailed explanation of this function will be given justbelow. First. a user of the system is recommended to search‘names-of-classification-items’ that characterize well the ‘consequence’of the

[2417] ‘hypothetical_proposition_(—)

[2418]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2419] by using the object-oriented knowledge base system.

[2420] For the user of the system to make such a search, it isrecommended that he should use @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] on thebasis of the natural words in the ‘consequence’. In other words, it isrecommended that a user of the system to make such a search should makea list of the ‘names-of-classification-items’ that are associated with

[2421] natural words which characterizes the ‘consequence’ of the

[2422] ‘hypothetical_proposition_(—)

[2423]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2424] by using

[2425] “means for making a list of ‘names-of-classification-items’ranked in order of hit frequency”

[2426] (See FIG. 14).

[2427] And it is recommended that,

[2428] as the basis of data on which

[2429] “means for making a list of ‘names-of-classification-items’ranked in order of hit frequency”

[2430] is carried out,

[2431] keys described using ‘means for storing data providing theability of association’,

[2432] and/or

[2433] keys described using ‘means for storing the list of lexicalmeanings of a natural word’ should be mainly used. Of course, other keysin the object-oriented knowledge base also may be used (See FIG. 14).

[2434] See Formula. 8A and Formula. 8B for a schematic quasi-C code asan example of embodiment of @[algorithm of making a list of‘descriptors’ ranked in order of hit frequency].

[2435] And if the user of the system finds some usable‘names-of-classification-items’ out of the list, then, it is recommendedthat the user of the system should optimize such usable‘names-of-classification-items’.

[2436] The way of the optimization depends whether the user of thesystem is willing to make a concentrated “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’” and/or willing to makean exhaustive “retrieval of the ‘hypothetical propositions to be used inbackward reasoning’”.

[2437] This situation corresponds to a sentence in the quasi-C functionin Formula. 2, if(some usable ‘names-of-classification-items’ areretrieved) {  if(concentrated retrieval is to be carried out)  {  Optimize_usable_‘names-of-classification-items’_for_(—)  concentrated_(—)   “retrieval of the ‘hypothetical propositions to beused in backward   reasoning’”_by_using_‘classification_table’_( ) ;_(—)  confirming_the_optimized_‘names-of-classification-items’_(—)  by_using_keys_and_records_of_the_corresponding_data_(—)   if_necessary( ) ; _(—)  }  else if(exhaustive retrieval is to be carried out)  {  Optimize_usable_‘names-of-classification-items’_for_(—)  exhaustive_(—)   “retrieval of the ‘hypothetical propositions to beused in backward   reasoning’”_by_using_‘classification_table’( ) ;_(—)  confirming_the_optimized_‘names-of-classification-items’_(—)  by_using_keys_and_records_of_the_corresponding_data   _if_necessary () ; _(—)  } }

Lexical Definition of “Means for Getting ‘Names-of-Classification-Items’that are Used to Make a Query to Get the “Candidates of the‘Rules-for-Reasoning’”” (Part 2. In the Case of Backward Reasoning

[2438] The procedure described by the sentence just mentioned above, inthe quasi-C function in Formula. 2,Search_usable_‘names-of-classification-items’_(—)which_represent_suitably_the_(—) ‘consequence’_(—) of_the_(—)‘hypothetical_proposition_(—) which_is_the_target_of_the_present_(—)step_of_opportunistic_reasoning’_(—) by_using_@[algorithm of lookingthrough ‘names-of-classification-items’ in the order of hit frequency]_() ;_(—) if(some usable ‘names-of-classification-items’ are retrieved) { if(concentrated retrieval is to be carried out)  {  Optimize_usable_(—)  ‘names-of-classification-items’_for_concentrated_(—)   “retrieval ofthe ‘hypothetical propositions to be used in backward  reasoning’”_by_using_‘classification_table’_( ) ;_(—)  confirming_the_optimized_‘names-of-classification-items’_(—)  by_using_keys_and_records_of_the_corresponding_data_(—)   if_necessary( ) ; _(—)  }  else if(exhaustive retrieval is to be carried out)  {  Optimize_usable_‘names-of-classification-items’_for_exhaustive_(—)  “retrieval of the ‘hypothetical propositions to be used in backward  reasoning’”_by_using_‘classification_table’( ) ;_(—)  confirming_the_optimized_‘names-of-classification-items’_(—)  by_using_keys_and_records_of_the_corresponding_data   _if_necessary () ; _(—)  } }

[2439] is a “means for getting ‘names-of-classification-items’ that areused to make a query to get the “candidates of the‘rules-for-reasoning’””, in the case of backward reasoning.

[2440] The lexical definition of ‘names-of-classification-items’ thatare used to make a query to get the “candidates of the‘rules-for-reasoning'” (part 2. In the case of backward reasoning), willbe given later in the present invention.

[2441] The recommended constitution how “means for getting‘names-of-classification-items’ that are used to make a query to get the“candidates of the ‘rules-for-reasoning’”” is embodied, is schematicallyshown in FIG. 14.

3.3.11.2.3.2.2.2. Search of ‘Names-of-Classification-Items’ to be Usedfor a Concentrated “Retrieval of the ‘Hypothetical Propositions PossiblyUsed in Backward Reasoning’”

[2442] First I give an explanation about the sentence in Formula. 2,which I have shown just above, if(some usable‘names-of-classification-items’ are retrieved) {  if(concentratedretrieval is to be carried out)  {  Optimize_usable_‘names-of-classification-items’_for_(—)  concentrated_(—)   “retrieval of the ‘hypothetical propositions to beused in backward   reasoning’”_by_using_‘classification_table’_( ) ;_(—)

[2443] That is, if the user of the system is willing to make aconcentrated “retrieval of the ‘hypothetical propositions to be used inbackward reasoning’”, then, it is recommended that he should search aslower class ‘names-of-classification-items’ as possible, that representsthe most precisely and specifically the contents of the ‘consequence’ ofthe

[2444] ‘hypothetical_proposition_(—)

[2445]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2446] In ideal cases when the maker of the contents has alreadyassigned the most specific ‘names-of-classification-items’ to the‘consequence’, the user of the system, of course, need not have to comeagain after the maker of the system.

[2447] For such optimizations, it is recommended that the user of thesystem should use ‘classification table’; That is, if the user of thesystem is wiling to make a concentrated “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’”, then, it is recommendedthat he should search ‘names-of-classification-items’ which are not onlyusable but also lower class of the usable‘names-of-classification-items’ from the ‘classification table’. In thiscase, it is recommended that he should use a computer flexibly to searchand list ‘names-of-classification-items’ that are lower class of theusable ‘names-of-classification-items’. A quasi-C code which outlinesthe procedure to search and list the ‘names-of-classification-items’that are lower class (by one rank) of a ‘name-of-classification-item’ isshown in Formula. 14. It is recommended that the user of the systemshould use flexibly the procedure which is outlined as a quasi-C code inFormula. 14.

[2448] As an example of such a flexible use, the procedure which isoutlined as a quasi-C code in Formula. 14 may be used recursively andcompletely, by using the usable ‘names-of-classification-items’ as theoriginal seeds. That is, one may regard all the usable‘names-of-classification-items’ included in the list of the‘names-of-classification-items’ that are lower class (by one rank) of aseed ‘name-of-classification-item’, as another seed‘name-of-classification-item’. If this procedure is continued until nousable lower class ‘name-of-classification-item’ can be listed any more,then, as the result, all the usable ‘name-of-classification-items’ thatare lower class of the usable ‘name-of-classification-items’ can beobtained.

[2449] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in backward reasoning’”, then,it is recommended that he should confirm the usableness

[2450] (i.e. to be usable to characterize well the contents of the‘conclusion’ of the ‘hypothetical_proposition_(—)

[2451]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’),

[2452] of the ‘name-of-classification-items’ thus obtained by watchingthe keys and the records that are associated with the‘name-of-classification-items’. If the user of the system contrives hisoriginal ‘hypothetical proposition’ in this stage or other, then, ofcourse, he may provide his original ‘hypothetical proposition’ as aknowledge to be used for the system's inference during his ownoperation. It is recommended in this case, too, that a knowledgeengineer should judge such a ‘hypothetical proposition’ whether to be ofuniversal use and/or to be only of special use.

[2453] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above,confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_(—) if_necessary ( ); _(—) }.

3.3.11.2.3.2.2.3. Search of ‘Names-of-Classification-Items’ to be Usedfor a Exhaustive “Retrieval of the ‘Hypothetical Propositions PossiblyUsed in Backward Reasoning’”

[2454] Second, I give an explanation about the sentence in Formula. 2,which I have shown just above, else if(exhaustive retrieval is to becarried out) {Optimize_usable_‘names-of-classification-items’_for_exhaustive_(—)“retrieval of the ‘hypothetical propositions to be used in backwardreasoning’”_by_using_‘classification_table’( ) ;_.

[2455] That is, if the user of the system is willing to make aexhaustive “retrieval of the ‘hypothetical propositions to be used inbackward reasoning’”, then, it is recommended that he should search ashigher class of ‘names-of-classification-items’ as possible thatrepresent most universally the contents of the ‘consequence’ of the

[2456] ‘hypothetical_proposition_(—)

[2457]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2458] For such optimizations, it is recommended that the user of thesystem should use ‘classification table’; That is, if the user of thesystem is wiling to make an exhaustive “retrieval of the ‘hypotheticalpropositions to be used in backward reasoning’”, then, it is recommendedthat he should search the highest class of‘names-of-classification-items’ of the usable‘names-of-classification-items’, from the ‘classification table’ In thiscase, it is recommended that he should use a computer to search and listthe ‘names-of-classification-items’ that are higher class of the usable‘name-of-classification-item’. A quasi-C code which outlines theprocedure to search and list the ‘names-of-classification-items’ thatare higher class (by one rank) of a ‘name-of-classification-item’ isshown in Formula. 11. It is recommended that the user of the systemshould use flexibly the procedure which is outlined as a quasi-C code inFormula. 11.

[2459] As an example of such a flexible use, the procedure which isoutlined as a quasi-C code in Formula. 11 may be used recursively andcompletely, by using the usable ‘names-of-classification-items’ as theoriginal seeds. That is, one may regard all the usable‘names-of-classification-items’ included in the list of the‘names-of-classification-items’ that are higher class (by one rank) of aseed ‘name-of-classification-item’, as another seed‘name-of-classification-item’. If this procedure is continued until nousable higher class ‘names-of-classification-items’ can not be listedany more, then, as the result, all the usable‘names-of-classification-items’ that are higher class of the usable‘names-of-classification-items’ can be obtained.

[2460] If the user of the system is going to make a strict “retrieval ofthe ‘hypothetical propositions to be used in backward reasoning’”, it isrecommended that he should confirm the usableness

[2461] (i.e. to be usable to characterize well the contents of the‘conclusion’ of the ‘hypothetical_proposition_(—)

[2462]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’),

[2463] of the ‘names-of-classification-items’ thus obtained by watchingthe keys and the records that are associated with the‘names-of-classification-items’. If the user of the system contrives hisoriginal ‘hypothetical proposition’ in this stage, then of course, hemay provide his original ‘hypothetical proposition’ as a knowledge to beused for the system's inference during his own operation. It isrecommended in this case, too, that a knowledge engineer should judgesuch a ‘hypothetical proposition’ whether to be of universal use and/orto be only of special use.

[2464] This procedure corresponds to a sentence in the quasi-C functionin Formula. 2, which I have mentioned just above,confirming_the_optimized_‘names-of-classification-items’_(—)by_using_keys_and_records_of_the_corresponding_data_(—) if_necessary ( ); _(—) } }.

[2465] In some case in which no appropriate ‘descriptors’ are found, itis recommended that the user of the system should find and use‘next-best-natural-nouns’ instead of ‘descriptors’ in the “retrieval ofthe ‘hypothetical propositions to be used in backward reasoning’” of thepresent execution of the loop body in the ‘while( ){}’ iterationstatement.

[2466] In some case in which no appropriate‘names-of-classification-items’ are found, it is recommended that theuser of the system should find and use ‘next-best-natural-verbs’ insteadof ‘names-of-classification-items’ in the “retrieval of the‘hypothetical propositions to be used in backward reasoning’” of thepresent execution of the loop body in the ‘while( ){}’ iterationstatement.

[2467] If ‘descriptors’ and/or ‘algorithm-of-process’ characterizingwell the ‘consequence’ of the

[2468] ‘hypothetical_proposition_(—)

[2469]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2470] are found,

[2471] and/or

[2472] a user of the system contrives ‘next-best-natural-nouns’ and/or‘next-best-natural-verbs’ characterizing well the ‘consequence’ of the

[2473] ‘hypothetical_proposition_(—)

[2474]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2475] then,

[2476] it is recommended that backward reasoning should be carried outin the present step of opportunistic reasoning (i.e. in the presentexecution of the loop body in the ‘while( ){}’ iteration statement). Ifand when the ‘consequence’ of the hypothetical proposition is an emptyset, backward reasoning can not of course be carried out.

[2477] The situation described here above is shown in Formula. 2 as,if(‘descriptors’ and/or ‘names-of-classification-items’describing the‘consequence’ of the ‘hypothetical_proposition_(—)which_is_the_target_of_the_present_(—) step_of_opportunistic_reasoning’are found and/or a user of the system contrives a good‘next-best-natural-nouns’ and/or a good ‘next-best-natural-verbs’) {Carry_out_backward_reasoning_and_(—)Determine_the_hypothetical_propositions_which_is_to_be_the_(—)target_of_the_next_step_of_opportunistic_reasoning( ) ;_(—) }.

[2478] The recommended way how a backward reasoning should be carriedout in the present step of opportunistic reasoning is shown in Formula.4. Detailed descriptions will be given later in the present invention.

3.3.11.2.3.3. The Case When Both Preparation for a “Retrieval of the‘Hypothetical Propositions Possibly Used in Forward Reasoning’” andPreparation for a “Retrieval of the ‘Hypothetical Propositions PossiblyUsed in Backward Reasoning’” Have Failed

[2479] If and when usable ‘descriptors’, ‘next-best-natural-nouns’,‘names-of-classification-items’, and/or, ‘next-best-natural-verbs’ cannot be found for either ‘presupposition’ or ‘consequence’ of the

[2480] ‘hypothetical_proposition_(—)

[2481]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2482] can not be found,

[2483] then,

[2484] it is recommended that

[2485] the “decision concerning whether forward reasoning and/orbackward reasoning should be carried out” made in previous steps ofopportunistic reasoning, should be changed.

[2486] Speaking more generally, in some realistic case, which is notshown completely in the quasi-C code of Formula. 2, final judgment aboutwhether forward reasoning and/or backward reasoning should be carriedout, can not always be correctly given in the present step ofopportunistic reasoning. And the present decision should be revisedflexibly even in many steps after, if and when one encounters heavydeadlock and/or uncontrollable combinatorial explosion. In other words,if and when one encounters heavy deadlock and/or uncontrollablecombinatorial explosion, then it is recommended that one should change adecision “whether forward reasoning and/or backward reasoning should becarried out” made in one of the previous steps of opportunisticreasoning.

[2487] This corresponds to the sentence in Formula. 2, else {Change_the_(—) “decision_concerning_whether_forward_reasoning_and/or_(—)backward_reasoning_should_be_carriedout”_made_in_previous_steps_of_opportunistic_reasoning( ) ;_(—) }, and/orif(@[algorithm of broadening out the target ‘descriptors’ and/or target‘names-of-classification-items’] dose not work well) { Change_the_(—)“decision_concerning_whether_forward_reasoning_and/or_(—)backward_reasoning_should_be_carriedout”_(—)made_in_previous_steps_of_opportunistic_reasoning( ) ;_(—) }.

[2488] It is recommended that

[2489] the hypothetical proposition that is to be used as the target ofthe next step of opportunistic reasoning

[2490] should be determined

[2491] at the end of the present step of opportunistic reasoning.

[2492] But this procedure is not carried out in the main routine, whichis represented in Formula. 2, but is carried out at the end of the‘subroutine for forward reasoning’, which is represented in Formula. 3and/or is carried out at the end of the ‘subroutine for backwardreasoning’, which is represented in Formula. 4. The detail will bedescribed later in the present invention.

3.3.11.2.3.4. Sub Routines 3.3.11.2.3.4.1. Sub Routine for ForwardReasoning 3.3.11.2.3.4.1.0. Terminology

[2493] The recommended way how a forward reasoning should be carried outin the present step of opportunistic reasoning is shown in Formula. 3.

[2494] Before describing details of the procedure for forward reasoning,here, I give a lexical definition of the term, ‘hypotheticalpropositions to be used in forward reasoning’.

Lexical Definition of ‘Hypothetical Proposition to be Used in ForwardReasoning’

[2495] In ideal cases,

[2496] first of all,

[2497] a ‘hypothetical proposition to be used in forward reasoning’

[2498] is

[2499] usually a hypothetical proposition registered in an‘object-oriented knowledge base’ disclosed in the present invention.

[2500] and, second,

[2501] the ‘presupposition’ of the ‘hypothetical proposition to be usedin forward reasoning’

[2502] is

[2503] a proposition that can be derived

[2504] from the ‘presupposition’ of the

[2505] ‘hypothetical_proposition_(—)

[2506]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’, byusing the @[algorithm of sentence based object-oriented categoricalsyllogism] and/or by @[algorithm of sentence based object-orientedhypothetical syllogism].

Lexical Definition of ‘Rules-for-Reasoning’ (Part 1. In the Case ofForward Reasoning)

[2507] A rule on the basis of which a reasoning is carried out in eachstep of opportunistic reasoning, is a ‘rules-for-reasoning’. Especiallywhen forward reasoning is being carried out, I call ‘hypotheticalproposition to be used in forward reasoning’ a ‘rule-for-reasoning’.

[2508] In the case of forward reasoning, first, it is recommended that‘descriptors’ and/or ‘names-of-classification-items’ that have beenoptimized in the main routine should be used to retrieve the‘hypothetical propositions possibly used in forward reasoning’ if atleast one such optimized ‘descriptor’ and/or optimized‘names-of-classification-item’ has been successfully obtained.

[2509] Speaking more strictly, it is recommended that the user of anobject-oriented knowledge base system disclosed in the present inventionshould retrieve hypothetical propositions whose ‘presupposition’contains all of and/or part of the optimized ‘descriptors’ and/orcontains all of and/or part of the optimized‘names-of-classification-items’, from the ‘object-oriented knowledgebase’ disclosed in the present invention.

[2510] In a word, the ‘presuppositions’ of the hypothetical propositionsare chosen as the target of a Boolean search using a query made of theoptimized ‘descriptors’ and/or of the optimized‘names-of-classification-items’.

[2511] Of course, not all the hypothetical propositions thus retrievedare a ‘hypothetical proposition to be used in forward reasoning’. Inother words, all the hypothetical propositions thus retrieved are judgedto be a ‘hypothetical propositions possibly used in forward reasoning’,but are not necessarily judged to be a ‘hypothetical proposition to beused in forward reasoning’.

[2512] And else if no such optimized ‘descriptors’ and/or optimized‘names-of-classification-items’ have been successfully obtained, then,it is recommended that ‘next-best-natural-nouns’ and/or‘next-best-natural-verbs’ should be used to retrieve the ‘hypotheticalpropositions possibly used in forward reasoning’. Speaking morestrictly, it is recommended that the user of the system should retrievehypothetical propositions whose ‘presupposition’ contains all of and/orpart of the ‘next-best-natural-nouns’ and/or contains all of and/or partof the ‘next-best-natural-verbs’, from the ‘object-oriented knowledgebase’ disclosed in the present invention. In a word, the‘presupposition’ of the hypothetical propositions are chosen as thetarget of a Boolean search using ‘next-best-natural-nouns’ and/or‘next-best-natural-verbs’.

Lexical Definition of ‘Hypothetical Propositions Possibly Used inForward Reasoning’

[2513] In ideal cases,

[2514] first of all,

[2515] a ‘hypothetical propositions possibly used in forward reasoning’is usually a hypothetical proposition registered in an ‘object-orientedknowledge base’ disclosed in the present invention.

[2516] And second, the ‘presupposition’ of the ‘hypotheticalpropositions possibly used in forward reasoning’

[2517] is

[2518] a proposition that is retrieved during a Boolean search using aquery made of,

[2519] ‘descriptors’, ‘names-of-classification-items’,‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’

[2520] which characterize the presupposition of

[2521] ‘hypothetical_proposition_(—)

[2522]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

Lexical Definition of “Candidates of the ‘Rules-for-Reasoning’” (Part 1.In the Case of Forward Reasoning)

[2523] A rule which can be possibly used as a ‘rules-for-reasoning’ iscalled a “candidates of the ‘rules-for-reasoning’”. Especially in thecase when forward reasoning is being carried out, I call ‘Hypotheticalpropositions possibly used in forward reasoning’ a “candidates of the‘rules-for-reasoning’”.

[2524] The lexical definition of ‘rules-for-reasoning’ in the case offorward reasoning has already given in the present invention.

[2525] The contents of the lexical definition of ‘hypotheticalpropositions possibly used in forward reasoning’ plus the lexicaldefinition of “candidates of the ‘rules-for-reasoning’” is schematicallyshown in FIG. 15.

Lexical Definition of Query to Get the “Candidates of the‘Rules-for-Reasoning’” (Part 1. In the Case of Forward Reasoning)

[2526] The query which appeared in the lexical definition of‘hypothetical propositions possibly used in forward reasoning’ is aquery to get the “candidates of the ‘rules-for-reasoning’”, in the caseof forward reasoning.

[2527] The lexical definition of “candidates of the‘rules-for-reasoning’”, in the case of forward reasoning has just givenin the present invention.

Lexical Definition of ‘Descriptors’ That are Used to Make a Query to Getthe “Candidates of the ‘Rules-for-Reasoning’” (Part 1. In the Case ofForward Reasoning)

[2528] The ‘descriptors’ which appeared in the lexical definition of‘hypothetical propositions possibly used in forward reasoning’ are‘descriptors’ that are used to make a query to get the “candidates ofthe ‘rules-for-reasoning’”, in the case of forward reasoning.

[2529] The lexical definition of query to get the “candidates of the‘rules-for-reasoning’”, in the case of forward reasoning has just beengiven in the present invention.

Lexical Definition of ‘Names-of-Classification-Items’ that are Used toMake a Query to Get the “Candidates of the ‘Rules-for-Reasoning’”(Part 1. In the Case of Forward Reasoning)

[2530] The ‘names-of-classification-items’ which appeared in the lexicaldefinition of ‘hypothetical propositions possibly used in forwardreasoning’ are

[2531] names-of-classification-items’ that are used to make a query toget the “candidates of the ‘rules-for-reasoning’”, in the case offorward reasoning (See FIG. 12).

[2532] The lexical definition of query to get the “candidates of the‘rules-for-reasoning’”, in the case of forward reasoning has just beengiven in the present invention.

3.3.11.2.3.4.1.1. Retrieval of “Candidates of the ‘Rules-for-Reasoning’”

[2533] At the beginning of the procedure for forward reasoning, it isrecommended that rules expressed as a key described in “sentence patternof physical and/or mathematical rules” and/or in “sentence pattern offunction” should be used as the target of the retrieval of the‘hypothetical propositions possibly used in forward reasoning’. Thisretrieval corresponds to the sentence in Formula. 3,

[2534]Retrieve_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_by_using_(—)

[2535]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’() ;_.

[2536] It is recommended that keys described in the “sentence pattern ofinstances of solving problems” should also be used as the target of theretrieval of the ‘hypothetical propositions possibly used in forwardreasoning’.

[2537] In the case when a key described in the “sentence pattern ofphysical and/or mathematical rules”, and/or in the “sentence pattern offunction” is used as the target of the retrieval, then, the keys thatare retrieved, of course, may be used as it is, as a ‘hypotheticalpropositions possibly used in forward reasoning’. However, in the casewhen a key described in “sentence pattern of instances of solvingproblems” are used as the target of the retrieval of the ‘hypotheticalpropositions possibly used in forward reasoning’, the situation is notso simple;

[2538] First, it is recommended that a key described in “sentencepattern of instances of solving problems” should be retrieved, if thekey in “sentence pattern of instances of solving problems” contains atleast one hypothetical proposition which is judged to be a ‘hypotheticalpropositions possibly used in forward reasoning’. It is recommended thatin this retrieval, too, optimized ‘descriptors’, optimized‘names-of-classification-items’, ‘next-best-natural-nouns’, and/or,‘next-best-natural-verbs’ should be used.

[2539] If and when such a key described in “sentence pattern ofinstances of solving problems” is found, then, it is recommended thatfollowing procedure should be carried out by using the key:

[2540] That is, the “‘hypothetical propositions possibly used in forwardreasoning’ in the key”, of course, may be used as it is as a‘hypothetical propositions possibly used in forward reasoning’.

[2541] In addition to this,

[2542] it is recommended that one should compose a “hand madehypothetical proposition” which is used as a ‘hypothetical propositionspossibly used in forward reasoning’, by combining

[2543] the “‘hypothetical propositions possibly used in forwardreasoning’ in the key” and

[2544] a “‘logical chain composed of hypothetical propositions‘ in thekey” which begins just below the “‘hypothetical propositions possiblyused in forward reasoning’ in the key”.

[2545] More explicitly speaking,

[2546] if

[2547] a “hypothetical proposition”

[2548] equals to a hypothetical proposition,

[2549] whose ‘presupposition’ equals to the ‘presupposition’ of the“‘hypothetical propositions possibly used in forward reasoning’ in thekey”,

[2550] and

[2551] whose ‘consequence’ equals to the ‘consequence’ of one of thehypothetical propositions

[2552] contained in

[2553] the “‘logical chain composed of hypothetical propositions’ in thekey”,

[2554] the hypothetical proposition on the top of which equals to the“‘hypothetical propositions possibly used in forward reasoning’ in thekey”,

[2555] then,

[2556] the “hypothetical proposition” is judged to be a “hand madehypothetical proposition” used as a ‘hypothetical propositions possiblyused in forward reasoning’.

[2557] If more than two such “‘logical chains of hypotheticalpropositions’ in the key” exist, then, of course, the “hand madehypothetical propositions” may be constructed for each of them.

[2558] The procedure described above corresponds to the sentence inFormula. 3,

[2559] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_by_using_(—)

[2560]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2561] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[2562]and_make_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’();_(—)

[2563] Here, I give a lexical definition of “retrieval of the“candidates of the ‘rules-for-reasoning’””.

[2564] It should be noted that how a

[2565] “retrieval of the “candidates of the ‘rules-for-reasoning’””

[2566] is used in

[2567] each step of opportunistic reasoning

[2568] has already explained in “§ 3.3.11.2.3.0. Overview’”.

Lexical Definition of “Retrieval of the “Candidates of the‘Rules-for-Reasoning’”” (Part 1. In the Case of Forward Reasoning)

[2569] The procedure expressed by sentences in Formula. 3, justdescribed above,

[2570]Retrieve_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_by_using_(—)

[2571]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’();_.

[2572] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_by_using_(—)

[2573]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_and_make_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’();_(—)

[2574] is a recommended procedure for “retrieval of the “candidates ofthe ‘rules-for-reasoning’”” in the case of forward reasoning.

[2575] As described above,

[2576] it is recommended that,

[2577] as the basis of data from which

[2578] “candidates of the ‘rules-for-reasoning’”

[2579] are retrieved,

[2580] keys described using ‘means for storing data used as rules’ ,

[2581] and/or

[2582] keys described using ‘means for storing data about instances ofsolving problems’ should be mainly used. Of course, other keys in theobject-oriented knowledge base also may be used (See FIG. 15).

3.3.11.2.3.4.1.2. Means for Retrieving Directly HypotheticalPropositions to be Used in Forward Reasoning

[2583] As a first measures to deal with the case when too many‘hypothetical propositions possibly used in forward reasoning’ areretrieved, i.e. when combinatorial explosion occurs, first, it isrecommended that the ‘hypothetical propositions to be used in forwardreasoning’, not the ‘hypothetical propositions possibly used in forwardreasoning’, should be directly retrieved. The procedure described herecorresponds to the sentence in Formula. 3, if( too many ‘hypotheticalpropositions possibly used in forward reasoning’ are retrieved, i.e. acombinatorial explosion occurs) { Retrieve_directly_(—)‘Hypothetical_propositions_to_be_used_in_forward_(—) reasoning’( );_.

[2584] The last function in this sentence is a function that expresses@[algorithm of retrieving directly ‘Hypothetical propositions to be usedin forward reasoning’].

Lexical Definition of @[Algorithm of Retrieving Directly ‘HypotheticalPropositions to be Used in Forward Reasoning’]

[2585] An algorithm

[2586] with which

[2587] all the ‘hypothetical propositions’ in the knowledge base

[2588] whose ‘presupposition’ is judged to be derived from the

[2589] ‘presupposition’ of the

[2590] ‘hypothetical proposition which is the target of the present stepof opportunistic reasoning’,

[2591] by using

[2592] @[algorithm of sentence based object-oriented categoricalsyllogism] and/or

[2593] @[algorithm of sentence based object-oriented hypotheticalsyllogism], are retrieved,

[2594] is @[algorithm of retrieving directly ‘Hypothetical propositionsto be used in forward reasoning’].

[2595] And

[2596] in the case when a key described in “sentence pattern ofinstances of solving problems” which contains at least one hypotheticalproposition which is judged to be a ‘hypothetical propositions to beused in forward reasoning’, is regarded as the target of the retrieval,

[2597] it is recommended

[2598] that one should compose a “hand made hypothetical proposition”which is used as a ‘hypothetical propositions to be used in forwardreasoning’,

[2599] by combining

[2600] the “‘hypothetical propositions to be used in forward reasoning’in the key” and

[2601] a “‘logical chain composed of hypothetical propositions’ in thekey” which begins just below the “‘hypothetical propositions to be usedin forward reasoning’ in the key”.

[2602] This way to obtain a “hand made hypothetical proposition” whichis used as a ‘hypothetical propositions to be used in forwardreasoning’, is @[algorithm of retrieving directly ‘Hypotheticalpropositions to be used in forward reasoning’].

Lexical Definition of Means for Retrieving Directly HypotheticalPropositions to be Used in Forward Reasoning (Part 1. In the Case ofForward Reasoning)

[2603] @[Algorithm of retrieving directly ‘Hypothetical propositions tobe used in forward reasoning‘] and/or something that stores theinformation of it, is a means for retrieving directly Hypotheticalpropositions to be used in forward reasoning.

Lexical Definition of Mechanism of Reasoning Used in an Object-OrientedKnowledge Base System Disclosed in the Present Invention

[2604] @[Algorithm of retrieving directly ‘Hypothetical propositions tobe used in forward reasoning’] is a mechanism of reasoning used in anobject-oriented knowledge base system disclosed in the presentinvention.

Lexical Definition of “Means for Retrieving Directly the‘Rules-for-Reasoning’” (Part 1. in the Case of Forward Reasoning

[2605] Means for retrieving directly Hypothetical propositions to beused in forward

[2606] reasoning

[2607] is equal to

[2608] a “means for retrieving directly the ‘rules-for-reasoning’”,

[2609] in the case when forward reasoning is carried out.

[2610] It should be noted that how a

[2611] “means for retrieving directly the ‘rules-for-reasoning’”

[2612] is carried out in

[2613] each step of opportunistic reasoning

[2614] has already explained in “§ 3.3.11.2.3.0. Overview”.

[2615] As mentioned just before in the lexical definition of @[algorithmof retrieving directly ‘Hypothetical propositions to be used in forwardreasoning’], it is recommended that ‘means for carrying out sentencebased object-oriented categorical syllogism’ and/or ‘means for carryingout sentence based object-oriented hypothetical syllogism’ should beused when “means for retrieving directly the ‘rules-for-reasoning’” isembodied (See FIG. 18). And as the basis of data on which “means forretrieving directly the ‘rules-for-reasoning’” is carried out, it isrecommended that ‘ideal thesaurus’, ‘ideal classification table’, and,‘hypothetical proposition which is the target of the present step ofopportunistic reasoning’ should be used (See FIG. 18).

[2616] And it is recommended that

[2617] keys described using ‘means for storing data used as rules’,

[2618] and/or,

[2619] keys described using ‘means for storing data about instances ofsolving problems’, should be used as the basis of data from which‘rules-for-reasoning’ should be retrieved.

[2620] Still more explicit explanation for the judgment made in@[algorithm of retrieving directly ‘Hypothetical propositions to be usedin forward reasoning’] is as follows:

[2621] If and when the ‘presupposition’ of the

[2622] ‘hypothetical_proposition_(—)

[2623]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2624] is a categorical proposition,

[2625] then, it is recommended that the user of the system should makeuse of the procedure outlined in Formula. 15A+Formula. 15B+Formula.15C+Formula. 15D, which are quasi-C code which outlines the procedure tojudge, when a major premise is given, whether a proposition is a“conclusion of a ‘sentence based object-oriented categorical syllogism’,which is described by @[algorithm of sentence based object-orientedcategorical syllogism]” or not. It should be noted that Formula. 15A isthe main part of the procedure. If and when the procedure outlined inFormula. 15A+Formula. 15B+Formula. 15C+Formula. 15D is made use of,

[2626] then

[2627] the ‘presupposition’ of the

[2628] ‘hypothetical_proposition_

[2629] which_is_the_target_of thepresent_step_of_opportunistic_reasoning’,

[2630] should be provided with as the ‘Major Premise’ for the procedureoutlined in Formula. 15A,

[2631] and

[2632] the ‘presupposition’ of a ‘hypothetical propositions’ in theknowledge base should be regarded as the proposition judged by theprocedure outlined in Formula. 1 15A, i.e. should be regarded as the‘Candidate For Conclusion’ in Formula. 15A. For detail, see Formula.15A, first, and then, see Formula. 15B+Formula. 15C+Formula. 15D, whichare linked to Formula. 15A.

[2633] And if and when the ‘presupposition’ of the

[2634]‘hypothetical_proposition_which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2635] is a hypothetical proposition,

[2636] then, it is recommended that the user of the system should makeuse of the procedure outlined in Formula. 16, which is a quasi-C codewhich outlines the procedure to judge whether a proposition is a“conclusion of a ‘hypothetical syllogism described by @[algorithm ofsentence based object-oriented hypothetical syllogism]’” or not when amajor premise is given. When this procedure outlined in Formula. 16 ismade use of,

[2637] then,

[2638] the ‘presupposition’ of the

[2639] ‘hypothetical_proposition_

[2640]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,should be provided with as the ‘Major Premise’ for the procedureoutlined in Formula. 16,

[2641] and

[2642] the ‘presupposition’ of a ‘hypothetical propositions’ in theknowledge base should be regarded as the proposition judged by theprocedure outlined in Formula. 1 6, i.e., should be regarded as the‘Candidate For Conclusion’.

[2643] For detail, see Formula. 16.

[2644] Here,

[2645] the procedure with which to use, in the step of the forwardreasoning, the keys described

[2646] in “sentence pattern of physical and/or mathematical rules”and/or in “sentence pattern of function” as a rule,

[2647] and the procedure with which, in the step of the forwardreasoning, to obtain a rule by using a key described in “sentencepattern of instances of solving problems”, both of which are used toimplement the function in Formula. 3,

[2648] Retrieve_directly

[2649] ‘Hypothetical_propositions_to_be_used_in_forward_reasoning’( );_,

[2650] are the same as

[2651] the procedure with which to use, in the step of the forwardreasoning, the keys described

[2652] in “sentence pattern of physical and/or mathematical rules”and/or in “sentence pattern of function” as a rule, and

[2653] the procedure with which, in the step of the forward reasoning,to obtain a rule by using a key described in “sentence pattern ofinstances of solving problems”, both of which I have already disclosedin the present invention to show the way to get ‘hypotheticalpropositions possibly used in forward reasoning’ by using optimized‘descriptors’, optimized ‘names-of-classification-items’,‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’, which Ihave described by now in the present invention,

3.3.11.2.3.4.1.3. ‘Means for Narrowing Down the Target ‘Descriptors’’and ‘Means for Narrowing Down the Target‘Names-of-Classification-Items’’

[2654] If and when the first direct measures described just abovefailed, then another measures should be taken to deal with the case whentoo many ‘hypothetical propositions possibly used in forward reasoning’are retrieved, i.e. when combinatorial explosion occurs.

[2655] That is, if and when the first direct measures failed, then it isrecommended that a process according to @[algorithm of narrowing downthe target ‘descriptors’ and/or target ‘names-of-classification-items’]should be carried out. The lexical definition of @[algorithm ofnarrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’], the lexical definition of ‘means fornarrowing down the target ‘descriptors”, and the ‘means for narrowingdown the target ‘names-of-classification-items” have already given inthe present invention.

[2656] The procedure described above corresponds to the sentence inFormula. 3, if(combinatorial explosion now and/or in the future stillcan not be avoided) { Revise_optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)‘next-best-natural-nouns’,_and/or,_next-best-natural-verbs’ by_(—)Carring_out_a_process_according_to_@[algorithm of narrowing down thetarget ‘descriptors’ and/or target‘names-of-classification-items’]_if_necessary_(—)And_Retrieve_again,_(—)‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_(—)by_using_the_revised_(—)‘descriptors’,_names-of-classification-items’,_(—)‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’( ) ;_(—)

[2657] The retrieval which is described in this sentence in Formula. 3as,

[2658] And_Retrieve_again,_(—)

[2659]’Hypothetical_propositions_possibly_used_in_forward_reasoning’_(—)

[2660] by_using_the_revised_(—)

[2661] ‘descriptors’,_‘names-of-classification-items’,_

[2662] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,

[2663] is used as the

[2664]

retrieval to be made in the present step

, which appears in the lexical definition of @[algorithm of narrowingdown the target ‘descriptors’ and/or target‘names-of-classification-items’].

[2665] And the retrieval which is described in the previous sentence inFormula. 3 as, Retrieve_keys_in_“sentence pattern of instances ofsolving problems”_(—)

[2666] by_using

[2667]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2668] ‘next-best-natural-nouns’, and/or,_‘next-best-natural-verbs’,_(—)

[2669]and_make_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’();_(—)

[2670] is used as the

[2671]

retrieval made in the preceding step

which appears in the lexical definition of @[algorithm of narrowing downthe target ‘descriptors’ and/or target ‘names-of-classification-items’].

[2672] That is, roughly speaking, in the process according to@[algorithm of narrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’], first, it is recommended thatreconsideration should be carried out about the optimization madepreviously in the main routine and/or made in the previous step ofopportunistic reasoning; That is, it is recommended that revisedoptimization should be made all over again for ‘descriptors’ and for‘names-of-classification-items’, and then, appropriately revised‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’ should becontrived all over again. Then, it is recommended that a new retrievalof ‘hypothetical propositions possibly used in forward reasoning’ byusing these revised ‘descriptors’, revised‘names-of-classification-items’, revised ‘next-best-natural-nouns’,and/or, revised ‘next-best-natural-verbs’ should be carried out. It isexpected that less number of ‘hypothetical propositions possibly used inforward reasoning’ should be obtained in the new retrieval, andtherefore, the combinatorial explosion should be avoided.

3.3.11.2.3.4.1.4. ‘Means for Fusing Propositions’

[2673] If the opportunistic reasoning should reach a deadlock in a laterstep of opportunistic reasoning, after the process according to@[algorithm of narrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’] has been carried out here in thepresent step of opportunistic reasoning,

[2674] then,

[2675] it is recommended that

[2676] the process carried out according to the @[algorithm of narrowingdown the target ‘descriptors’ and/or target‘names-of-classification-items’]

[2677] should be withdrawn,

[2678] and that process according to @[algorithm of fusing propositions]should be carried out. The lexical definition of @[algorithm of fusingpropositions] and ‘means for fusing propositions’ have already given inthe present invention.

[2679] Here,

[2680] as the

propositions which should be fused

which appears in the lexical definition of @[algorithm of fusingpropositions],

[2681] the ‘consequences’ of the ‘hypothetical propositions possiblyused in forward reasoning’ that were retrieved in the present step offorward reasoning before the process according to @[algorithm ofnarrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’] was carried out

[2682] should be used: In other words, ‘consequences’ of the‘hypothetical propositions possibly used in forward reasoning’ that wereretrieved in the following sentences in Formula. 3 in the present stepof forward reasoning,

[2683]Retrieve_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_by_using_(—)

[2684]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’();_(—)

[2685] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_by_using_(—)

[2686]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_

[2687] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[2688]and_make_‘Hypothetical_propositions_possibly_used_inforward_reasoning’();_should be used as the

propositions which should be fused

which appears in the lexical definition of @[algorithm of fusingpropositions].

[2689] And as the output of the procedure according to @[algorithm offusing propositions],

fused hypothetical propositions to be used in the present step offorward reasoning

is obtained.

[2690] The procedure described above corresponds to the sentence inFormula. 3, if(combinatorial explosion now andlor in the future stillcan not be avoided) {Withdraw_the_process_carried_out_according_to_the_(—) @[algorithm ofnarrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’] andCarry_out_a_process_according_to_(—) @[algorithm of fusingpropositions]_to_the_‘presuppositions’_(—)of_the_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_(—)if_necessary( ) ;_(—) }

[2691] Exactness of reasoning is often seriously lost when a procedureaccording to @[algorithm of fusing propositions] is carried out. So itis recommended that it should be carried out only in above mentionedsituation. Therefore, it is recommended that the @[algorithm of fusingpropositions] should be carried out only when combinatorial explosioncan not be avoided only by carrying out @[algorithm of narrowing downthe target ‘descriptors’ and/or target ‘names-of-classification-items’]and/or @[algorithm of narrowing down the target ‘descriptors’ and/ortarget ‘names-of-classification-items’] should be withdrawn in a laterstep.

3.3.11.2.3.4.1.5. ‘Means for Broadening Out the Target ‘Descriptors’’and ‘Means for Broadening Out the Target‘Names-of-Classification-Items’’

[2692] On the contrary when too less number of ‘hypotheticalpropositions to be used in reasoning of the present step’ are obtained,it is recommended that @[algorithm of broadening out the target‘descriptors’ and/or target ‘names-of-classification-items’] should becarried out. I have already given the lexical definition of @[algorithmof broadening out the target ‘descriptors’ and/or target‘names-of-classification-items’], the lexical definition of ‘means forbroadening out the target ‘descriptors’’, and the lexical definition of‘means for broadening out the target ‘names-of-classification-items’’,in the present invention.

[2693] The retrieval which is described in the sentence in Formula. 3as,

[2694] And_Retrieve_again,_(—)

[2695]‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_(—)

[2696] by_using_the_revised_(—)

[2697] ‘descriptors’,_‘names-of-classification-items’,_(—)

[2698] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,

[2699] is used as the

[2700]

retrieval to be made in the present step

which appears in the lexical definition of @[algorithm of broadening outthe target ‘descriptors’ and/or target ‘names-of-classification-items’].

[2701] And the retrieval which is described in the sentence in Formula.3 as,

[2702]Retrieve_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_by_using_(—)

[2703]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2704] ‘next-best-natural-nouns’,_and/or,_‘nexxt-best-natural-verbs’();_(—)

[2705] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_by_using_(—)

[2706]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_

[2707] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[2708]and_make_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’();_(—)

[2709] is used as the

[2710]

retrieval made in the preceding step

which appears in the lexical definition of @[algorithm of broadening outthe target ‘descriptors’ and/or target ‘names-of-classification-items’].

[2711] That is, roughly speaking, in the process according to@[algorithm of broadening out the target ‘descriptors’ and/or target‘names-of-classification-items’], first, it is recommended thatreconsideration should be carried out as for the optimization madepreviously in the main routine and/or made in the previous step ofopportunistic reasoning; That is, it is recommended that revisedoptimization should be made all over again for ‘descriptors’ and for‘names-of-classification-items’, and then, appropriately revised‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’ should becontrived all over again. Then, it is recommended that a new retrievalof ‘hypothetical propositions possibly used in forward reasoning’ byusing these revised ‘descriptors’, revised‘names-of-classification-items’, revised ‘next-best-natural-nouns’,and/or, ‘next-best-natural-verbs’ should be carried out.

[2712] As the result, it is expected that more number of ‘hypotheticalpropositions possibly used in forward reasoning’ should be obtained inthe revised retrieval, and therefore, the deadlock should be avoided.

[2713] This procedure given above corresponds to the sentence inFormula. 3, if( too less ‘hypothetical propositions possibly used inforward reasoning’ are retrieved) { Carry_out_@[algorithm of broadeningout the target ‘descriptors’ and/or target‘names-of-classification-items’] And_Retrieve_again,_(—)‘Hypothetical_propositions_possibly_used_in_forward_reasoning’( ) ;_.

3.3.11.2.3.4.1.6. “Means for Picking Up Only the ‘Rules-for-Reasoning’From the “Candidates of the ‘Rules-for-Reasoning’”” Lexical Definitionof @[Algorithm for Eliminating the Noise of the Retrieval of‘Hypothetical Propositions Possibly Used in Forward Reasoning’]

[2714] In cases when a procedure according to @[algorithm of fusingpropositions] has not been carried out, it is recommended that

[2715] whether

[2716] a ‘hypothetical propositions possibly used in forward reasoning’,which is retrieved in the present step of opportunistic reasoning

[2717] is strictly a

[2718] ‘hypothetical propositions to be used in forward reasoning’

[2719] or not,

[2720] should be verified by the user of the system.

[2721] And only ‘hypothetical propositions possibly used in forwardreasoning’ which is judged to be a ‘hypothetical propositions to be usedin forward reasoning’ by the user of the system should be used in theforward reasoning in the present step of forward reasoning.

[2722] The algorithm for this verification is @[algorithm foreliminating the noise of the retrieval of ‘hypothetical propositionspossibly used in forward reasoning’].

[2723] It is recommended that @[algorithm of sentence basedobject-oriented categorical syllogism] and/or @[algorithm of sentencebased object-oriented hypothetical syllogism] should be used to embody@[algorithm for eliminating the noise of the retrieval of ‘hypotheticalpropositions possibly used in forward reasoning]. And as the basis ofdata on which @[algorithm of sentence based object-oriented categoricalsyllogism] is carried out, it is recommended that ‘ideal thesaurus’,‘ideal classification table’, and, ‘hypothetical proposition which isthe target of the present step of opportunistic reasoning’ should beused. The detail of a recommended embodiment of @[algorithm foreliminating the noise of the retrieval of ‘hypothetical propositionspossibly used in forward reasoning] is outline by using a quasi-C codeshown in Formula. 17, for the ideal case in which the hypotheticalproposition to be used in forward reasoning is described exactlyaccording a mathematically perfect format.

Lexical Definition of Means for Eliminating the Noise of the Retrievalof Hypothetical Propositions Possibly Used in Forward Reasoning

[2724] @[Algorithm for eliminating the noise of the retrieval of‘hypothetical propositions possibly used in forward reasoning’] and/orsomething that stores the information of it, is a means for eliminatingthe noise of the retrieval of hypothetical propositions possibly used inforward reasoning.

Lexical Definition of “Means for Picking Up Only the‘Rules-for-Reasoning’ From the “Candidates of the‘Rules-for-Reasoning’”” (Part 1. In the case of Forward Reasoning)

[2725] Means for eliminating the noise of the retrieval of hypotheticalpropositions possibly used in forward reasoning

[2726] equals to

[2727] a “means for picking up only the ‘rules-for-reasoning’ from the“candidates of the ‘rules-for-reasoning’””,

[2728] in the case of forward reasoning.

[2729] It should be noted that how a

[2730] “means for picking up only the ‘rules-for-reasoning’ from the“candidates of the ‘rules-for-reasoning’””

[2731] is carried out in

[2732] each step of opportunistic reasoning

[2733] has already explained in “§ 3.3.11.2.3.0. Overview”.

[2734] As mentioned just before in the lexical definition of @[algorithmfor eliminating the noise of the retrieval of ‘hypothetical propositionspossibly used in forward reasoning’], it is recommended that ‘means forcarrying out sentence based object-oriented categorical syllogism’and/or ‘means for carrying out sentence based object-orientedhypothetical syllogism’ should be used when “means for picking up onlythe ‘rules-for-reasoning’ from the “candidates of the‘rules-for-reasoning’”” is embodied (See FIG. 16). And as the basis ofdata on which “means for picking up only the ‘rules-for-reasoning’ fromthe “candidates of the ‘rules-for-reasoning’”” is carried out, it isrecommended that ‘ideal thesaurus’, ‘ideal classification table’, and,‘hypothetical proposition which is the target of the present step ofopportunistic reasoning’ should be used (See FIG. 16).

[2735] In cases when a procedure according to @[algorithm of fusingpropositions] has been carried out, it is recommended that the

fused hypothetical propositions to be used in the present step offorward reasoning

should be used as it is as a ‘hypothetical proposition to be used inforward reasoning’ without any verification in the present step offorward reasoning. It is recommended that the verification should bedone after the final approximate answer will have been obtained, in thiscase.

[2736] These procedure corresponds to the sentence in Formula. 3,

[2737] Unless_@[algorithm of fusingpropositions]_has_not_been_carried_out_(—)

[2738]Choose_(—the)_‘hypothetical_propositions_to_be_used_in_forward_reasoning’_out_of_the_(—)

[2739]‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_(—)

[2740] And_else_if_@[algorithm of fusingpropositions)_has_been_carried_out_,

[2741] Use_

fused hypothetical propositions to be used in the present step offorward reasoning

_as_it_is

[2742]as_the_‘hypothetical_propositions_to_be_used_in_forward_reasoning’_();_(—)

3.3.11.2.3.4.1.7. @[Algorithm for Determining Hypothetical PropositionWhich is to be Used as the Target of the Next Step of OpportunisticReasoning] (Part 1: in the Case of Forward Reasoning)

[2743] It is recommended that hypothetical proposition which is to beused as the target of the next step of opportunistic reasoning

[2744] should be determined

[2745] on the basis of the

[2746] ‘hypothetical propositions to be used in forward reasoning’,which have been used in the ‘hypothetical syllogism carried out in thepresent step of opportunistic reasoning’ and on the basis of the

[2747] ‘hypothetical_proposition_(—)

[2748]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[2749] by using @[algorithm of sentence based object-orientedcategorical syllogism] and/or @[algorithm of sentence basedobject-oriented hypothetical syllogism].

[2750] This procedure corresponds to the sentence in Formula. 3,

[2751] Determine_(—l the)_hypothetical_propositions_(—)

[2752] which_are_to_be_used_(—)

[2753] as_the_target_of_the_next_step_of_opportunistic_reasoning_(—)

[2754] on_the_basis_(—)

[2755]of_the_‘hypothetical_propositions_to_be_used_in_forward_reasoning’_(—)

[2756] in_the_present_step_of_opportunistic_reasoning_(—)

[2757] and_(—)

[2758] of_a_hypothetical_proposition_(—)

[2759] which_is_the_target_of_the_present_step_of_forward_reasoning

[2760] by_using_(—)

[2761] @[algorithm of sentence based object-oriented categoricalsyllogism]and

[2762] @[algorithm of sentence based object-oriented hypotheticalsyllogism]_forward( );_.

[2763] Detail procedures for the instructions described by thissentences is given in Formula. 5A. This function shown just above is afunction representing @[algorithm for determining hypotheticalpropositions which are to be used as the target of the next step ofopportunistic reasoning] in the case of forward reasoning.

Lexical Definition of @[Algorithm for Determining HypotheticalProposition Which is to be Used as the Target of the Next Step ofOpportunistic Reasoning] (Part 1: in the Case of Forward Reasoning)

[2764] As I have just mentioned, a recommended procedures for@[algorithm for determining hypothetical proposition which is to be usedas the target of the next step of opportunistic reasoning] carried outin the case of forward reasoning is given in Formula. 5A.

[2765] That is, in the case of forward reasoning, a hypotheticalproposition that is to be used as the target of the next step ofopportunistic reasoning is, in an ideal case, a hypothetical proposition

[2766] whose ‘presupposition’ is equal to the ‘consequence’ of a‘hypothetical proposition to be used in forward reasoning’ that was usedin the reasoning of the present step of opportunistic reasoning,

[2767] and

[2768] whose ‘consequence’ is equal to the ‘consequence’ of the

[2769] ‘hypothetical_proposition_(—)

[2770]which_(is)_the_target_of_the_present_step_of_opportunistic_reasoning’.

[2771] @[Algorithm for determining hypothetical proposition which is tobe used as the target of the next step of opportunistic reasoning] is amechanism of reasoning used in an object-oriented knowledge base systemdisclosed in the present invention.

[2772] The problem is judged to be solved when at least one hypotheticalproposition that is to be used as the target of the next step ofopportunistic reasoning,

[2773] is judged to be trivial.

[2774] This corresponds to a sentence in Formula. 2, if( Problem issolved ) { break ;_(—) }.

3.3.11.2.3.4.2. Sub Routine for Backward Reasoning 3.3.11.2.3.4.2.0.Terminology

[2775] The recommended way how a backward reasoning should be carriedout in the present step of opportunistic reasoning is shown in Formula.4.

[2776] Before describing details of the procedure for backwardreasoning, here, I give a lexical definition of the term, ‘hypotheticalpropositions to be used in backward reasoning’.

Lexical Definition of ‘Hypothetical Proposition to be Used in BackwardReasoning’

[2777] In an ideal cases,

[2778] first of all,

[2779] a ‘hypothetical proposition to be used in backward reasoning’

[2780] is

[2781] usually a hypothetical proposition registered in an‘object-oriented knowledge base’ disclosed in the present invention.

[2782] and, second,

[2783] the ‘consequence’ of the ‘hypothetical proposition to be used inbackward reasoning’

[2784] is

[2785] a proposition from which

[2786] the ‘consequence’ of the

[2787] ‘hypothetical_proposition_(—)

[2788]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’ canbe derived,

[2789] by using the @[algorithm of sentence based object-orientedcategorical syllogism] and/or by @[algorithm of sentence basedobject-oriented hypothetical syllogism].

Lexical Definition of ‘Rules-for-Reasoning’ (Part 2. In the Case ofBackward Reasoning)

[2790] A rule on the basis of which a reasoning is carried out in eachstep of opportunistic reasoning, is a ‘rules-for-reasoning’. Especiallyin the case when backward reasoning is being carried out, I call‘hypothetical proposition to be used in backward reasoning’ a‘rule-for-reasoning’ (See FIG. 12).

[2791] In the case of backward reasoning, it is recommended that‘descriptors’ and/or ‘names-of-classification-items’ that have beenoptimized in the main routine should be used to retrieve ‘hypotheticalproposition possibly used in backward reasoning’ if at least one suchoptimized ‘descriptor’ and/or optimized ‘names-of-classification-item’has been successfully obtained.

[2792] Speaking more strictly, it is recommended that the user of anobject-oriented knowledge base system disclosed in the present inventionshould retrieve hypothetical propositions whose ‘consequence’ containsall of and/or part of the optimized ‘descriptors’ and/or contains all ofand/or part of the optimized ‘names-of-classification-items’, from the‘object-oriented knowledge base’ disclosed in the present invention.

[2793] In a word, the ‘consequence’ of the hypothetical propositions arechosen as the target of a Boolean search using a query made of theoptimized ‘descriptors’ and/or of the optimized‘names-of-classification-items’.

[2794] Of course, not all the hypothetical propositions thus retrievedare a ‘hypothetical proposition to be used in backward reasoning’. Inother words, all the hypothetical propositions thus retrieved are judgedto be a ‘hypothetical propositions possibly used in backward reasoning’,but are not necessarily judged to be a ‘hypothetical proposition to beused in backward reasoning’.

[2795] And else if no such optimized ‘descriptors’ and/or optimized‘names-of-classification-items’ have been successfully obtained, then,it is recommended that ‘next-best-natural-nouns’ and/or‘next-best-natural-verbs’ should be used to retrieve the ‘hypotheticalpropositions possibly used in backward reasoning’. Speaking morestrictly, it is recommended that the user of the system should retrievehypothetical propositions whose ‘consequence’ contains all of and/orpart of the ‘next-best-natural-nouns’ and/or contains all of and/or partof the ‘next-best-natural-verbs’, from the ‘object-oriented knowledgebase’ disclosed in the present invention. In a word, the ‘consequence’of the hypothetical propositions are chosen as the target of a Booleansearch using the ‘next-best-natural-nouns’ and/or‘next-best-natural-verbs’.

Lexical Definition of ‘Hypothetical Propositions Possibly Used inBackward Reasoning’

[2796] In ideal cases,

[2797] first of all,

[2798] a ‘hypothetical propositions possibly used in backward reasoning’is usually a hypothetical proposition registered in an ‘object-orientedknowledge base’ disclosed in the present invention.

[2799] And second, the ‘consequence’ of the ‘hypothetical propositionspossibly used in backward reasoning’

[2800] is

[2801] a proposition that is hit upon during a Boolean search using aquery made of, ‘descriptors’, ‘names-of-classification-items’,‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’

[2802] that characterize the consequence of‘hypothetical_proposition_(—)

[2803]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

Lexical Definition of “Candidates of the ‘Rules-for-Reasoning’” (Part 2:In the Case of Backward Reasoning)

[2804] A rule which can be possibly used as a ‘rules-for-reasoning’ iscalled a “candidates of the ‘rules-for-reasoning’”. Especially whenbackward reasoning is being carried out, I call ‘Hypotheticalpropositions possibly used in backward reasoning’ a “candidates of the‘rules-for-reasoning’”.

[2805] The lexical definition of ‘rules-for-reasoning’ in the case ofbackward reasoning has already given in the present invention.

[2806] The contents of the lexical definition of ‘hypotheticalpropositions possibly used in forward reasoning’ plus the lexicaldefinition of “candidates of the ‘rules-for-reasoning’” is schematicallyshown in FIG. 15.

Lexical Definition of Query to Get the “candidates of the‘Rules-for-Reasoning’” (Part 2: In the Case of Backward Reasoning)

[2807] The query which appeared in the lexical definition of‘hypothetical propositions possibly used in backward reasoning’ is aquery to get the “candidates of the ‘rules-for-reasoning’”, in the caseof backward reasoning.

[2808] The lexical definition of “candidates of the‘rules-for-reasoning’”, in the case of backward reasoning has just givenin the present invention.

Lexical Definition of ‘Descriptors’ that are Used to Make a Query to Getthe “Candidates of the ‘Rules-for-Reasoning’” (Part 2: In the Case ofBackward Reasoning)

[2809] The ‘descriptors’ which appeared in the lexical definition of‘hypothetical propositions possibly used in backward reasoning’ are‘descriptors’ that are used to make a query to get the “candidates ofthe ‘rules-for-reasoning’”, in the case of backward reasoning.

[2810] The lexical definition of query to get the “candidates of the‘rules-for-reasoning’”, in the case of backward reasoning has just beengiven in the present invention.

Lexical Definition of ‘Names-of-Classification-Items’ that are Used toMake a Query to Get the “Candidates of the ‘Rules-for-Reasoning’” (Part2: In the Case of Backward Reasoning)

[2811] The ‘names-of-classification-items’ which appeared in the lexicaldefinition of ‘hypothetical propositions possibly used in backwardreasoning’ are ‘names-of-classification-items’ that are used to make aquery to get the “candidates of the ‘rules-for-reasoning’”, in the caseof backward reasoning.

[2812] The lexical definition of query to get the “candidates of the‘rules-for-reasoning’”, in the case of backward reasoning has just beengiven in the present invention.

3.3.11.2.3.4.2.1. Retrieval of “Candidates of the‘Rules-for-Reasoning’”:

[2813] At the beginning of the procedure for forward reasoning, it isrecommended that rules expressed as a key described in “sentence patternof physical and/or mathematical rules” and/or in “sentence pattern offunction” should be used as the target of the retrieval of the‘hypothetical propositions possibly used in backward reasoning’. Thisretrieval corresponds to the sentence in Formula. 4,

[2814]Retrieve_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_by_using_(—)

[2815]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2816] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’();_.

[2817] It is recommended that keys described in the “sentence pattern ofinstances of solving problems” should also be used as the target of theretrieval of the ‘hypothetical propositions possibly used in backwardreasoning’.

[2818] In the case when keys in the “sentence pattern of physical and/ormathematical rules”, and/or “sentence pattern of function” are used asthe target of the retrieval, the keys that are retrieved, of course, maybe used as it is, as a ‘hypothetical propositions possibly used inbackward reasoning’. However, in the case when keys in “sentence patternof instances of solving problems” are used as the target of theretrieval of the ‘hypothetical propositions possibly used in backwardreasoning’, the situation is not so simple;

[2819] First, it is recommended that a key described in “sentencepattern of instances of solving problems” should be retrieved, if thekey in “sentence pattern of instances of solving problems” contains atleast one hypothetical proposition which is judged to be a ‘hypotheticalpropositions possibly used in backward reasoning’. It is recommendedthat in this retrieval, too, optimized ‘descriptors’, optimized‘names-of-classification-items’, ‘next-best-natural-nouns’, and/or,‘next-best-natural-verbs’ should be used.

[2820] If and when such a key described in “sentence pattern ofinstances of solving problems” is found, then, it is recommended thatfollowing procedure should be carried out by using the key:

[2821] That is, the “‘hypothetical propositions possibly used inbackward reasoning’ in the key”, of course, may be used as it is as a‘hypothetical propositions possibly used in backward reasoning’.

[2822] In addition to this,

[2823] it is recommended that one should compose a “hand madehypothetical proposition” which is used as a ‘hypothetical propositionspossibly used in backward reasoning’, by combining

[2824] the, “‘hypothetical propositions possibly used in backwardreasoning’ in the key”

[2825] and

[2826] a “‘logical chain composed of hypothetical propositions’ in thekey” which exist just up stream of the ‘“hypothetical propositionspossibly used in backward reasoning’ in the key”.

[2827] More explicitly speaking,

[2828] if a “hypothetical proposition”

[2829] equals to a hypothetical proposition,

[2830] whose ‘consequence’ equals to the ‘consequence’ of the“‘hypothetical propositions possibly used in backward reasoning’ in thekey”,

[2831] and

[2832] whose ‘presupposition’ equals to the ‘presupposition’ of one ofthe hypothetical proposition contained in

[2833] the “‘logical chain of hypothetical propositions’ in the key”,

[2834] the last hypothetical proposition of which equals to the“‘hypothetical propositions possibly used in backward reasoning‘ in thekey”,

[2835] then,

[2836] the “hypothetical proposition” is judged to be a “hand madehypothetical proposition” used as a ‘hypothetical propositions possiblyused in backward reasoning’.

[2837] If more than two such “‘logical chains of hypotheticalpropositions’ in the key” exist, then, of course, the “hand madehypothetical proposition” may be constructed for each of them.

[2838] The procedure described above corresponds to the sentence inFormula. 4,

[2839] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_by_using_(—)

[2840]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2841] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[2842]and_make_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’();_(—)

[2843] Here, I give a lexical definition of “retrieval of the“candidates of the ‘rules-for-reasoning’””.

[2844] It should be noted that how a

[2845] “retrieval of the “candidates of the ‘rules-for-reasoning’””

[2846] is used in

[2847] each step of opportunistic reasoning

[2848] has already explained in “§ 3.3.11.2.3.0. Overview”.

Lexical Definition of “Retrieval of the “Candidates of the‘Rules-for-Reasoning’”” (Part 2. In the Case of Backward Reasoning)

[2849] The procedure expressed by sentences in Formula. 4, justdescribed above,

[2850]Retrieve_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_by_using_(—)

[2851]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2852] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’();_.

[2853] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_by_using_(—)

[2854]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2855] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[2856]and_make_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’();_(—)

[2857] is a recommended procedure for “retrieval of the “candidates ofthe ‘rules-for-reasoning’”” in the case of backward reasoning.

[2858] As described above,

[2859] it is recommended that,

[2860] as the basis of data from which

[2861] “candidates of the ‘rules-for-reasoning’”

[2862] are retrieved,

[2863] keys described using ‘means for storing data used as rules’,

[2864] and/or

[2865] keys described using ‘means for storing data about instances ofsolving problems’ should be mainly used. Of course, other keys in theobject-oriented knowledge base also may be used (See FIG. 15).

3.3.11.2.3.4.2.2. Means for Retrieving Directly HypotheticalPropositions to be Used in Backward Reasoning

[2866] As a first measures to deal with the case when too many‘hypothetical propositions possibly used in backward reasoning’ areretrieved, i.e. when combinatorial explosion occurs, first, it isrecommended that the ‘hypothetical propositions to be used in backwardreasoning’, not the ‘hypothetical propositions possibly used in backwardreasoning’, should be directly retrieved. The procedure described abovecorresponds to the sentence in Formula. 4, if( too many ‘hypotheticalproposition possibly used in backward reasoning’ are retrieved, i.e. acombinatorial explosion occurs ) {  Retrieve_directly_(—) ‘Hypothetical_propositions_to_be_used_in_backward_(—)  reasoning’( );_(—)

[2867] The last function in this sentence is a function that expresses@[algorithm of retrieving directly ‘Hypothetical propositions to be usedin backward reasoning’].

Lexical Definition of @[Algorithm of Retrieving Directly ‘HypotheticalPropositions to be Used in Backward Reasoning’]

[2868] An algorithm

[2869] with which

[2870] all the ‘hypothetical propositions’ in the knowledge base

[2871] from whose ‘consequence’

[2872] the

[2873] ‘presupposition’ of the

[2874] ‘hypothetical_proposition_(—)

[2875]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2876] is judged to be derived

[2877] by using

[2878] @[algorithm of sentence based object-oriented categoricalsyllogism] and/or @[algorithm of sentence based object-orientedhypothetical syllogism],

[2879] are retrieved,

[2880] is @[algorithm of retrieving directly ‘Hypothetical propositionsto be used in backward reasoning’].

[2881] And

[2882] in the case when a key described in “sentence pattern ofinstances of solving problems” which contains at least one hypotheticalproposition which is judged to be a ‘hypothetical propositions to beused in backward reasoning’, is regarded as the target of the retrieval,

[2883] it is recommended

[2884] that one should compose a “hand made hypothetical proposition”which is used as a ‘hypothetical propositions to be used in backwardreasoning’,

[2885] by combining

[2886] the “‘hypothetical propositions to be used in backward reasoning’in the key”

[2887] and

[2888] a “‘logical chain composed of hypothetical propositions’ in thekey” which exist just up stream of the “‘hypothetical propositions to beused in backward reasoning’ in the key”.

[2889] This way to obtain a “hand made hypothetical proposition” whichis used as a ‘hypothetical propositions to be used in backwardreasoning’, is @[algorithm of retrieving directly ‘Hypotheticalpropositions to be used in backward reasoning’].

Lexical Definition of Means for Retrieving Directly HypotheticalPropositions to be Used in Backward Reasoning (Part 2. BackwardReasoning)

[2890] @[Algorithm of retrieving directly ‘Hypothetical propositions tobe used in backward reasoning’] and/or something that stores theinformation of it, is a means for retrieving directly Hypotheticalpropositions to be used in backward reasoning.

[2891] @[Algorithm of retrieving directly ‘Hypothetical propositions tobe used in backward reasoning’] is a mechanism of reasoning used in anobject-oriented knowledge base system disclosed in the presentinvention.

Lexical Definition of “Means for Retrieving Directly the‘Rules-for-Reasoning’” (Part 2. in the Case of Backward Reasoning

[2892] Means for retrieving directly Hypothetical propositions to beused in backward reasoning

[2893] is equal to

[2894] a “means for retrieving directly the ‘rules-for-reasoning’”

[2895] in the case when backward reasoning is carried out.

[2896] It should be noted that how a

[2897] “means for retrieving directly the ‘rules-for-reasoning’”

[2898] is carried out in

[2899] each step of opportunistic reasoning

[2900] has already explained in “§ 3.3.11.2.3.0. Overview”.

[2901] As mentioned just before in the lexical definition of @[algorithmof retrieving directly ‘Hypothetical propositions to be used in forwardreasoning’], it is recommended that ‘means for carrying out sentencebased object-oriented categorical syllogism’ and/or ‘means for carryingout sentence based object-oriented hypothetical syllogism’ should beused when “means for retrieving directly the ‘rules-for-reasoning’” isembodied (See FIG. 18). And as the basis of data on which “means forretrieving directly the ‘rules-for-reasoning’” is carried out, it isrecommended that ‘ideal thesaurus’, ‘ideal classification table’, and,‘hypothetical proposition which is the target of the present step ofopportunistic reasoning’ should be used (See FIG. 18).

[2902] And it is recommended that

[2903] keys described using ‘means for storing data used as rules’,

[2904] and/or

[2905] keys described using ‘means for storing data about instances ofsolving problems’,

[2906] should be used as

[2907] the basis of data from which ‘rules-for-reasoning’ should beretrieved.

[2908] Still more explicit explanation for the judgment made in@[algorithm of retrieving directly ‘Hypothetical propositions to be usedin backward reasoning’] is as follows:

[2909] If and when the ‘consequence’ of the

[2910] ‘hypothetical_proposition_(—)

[2911]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,

[2912] is a categorical proposition,

[2913] then, it is recommended that the user of the system should makeuse of the procedure outlined in Formula. 15A+Formula. 15B+Formula.15C+Formula. 15D, which is a quasi-C code which outlines the procedureto judge whether a proposition is a “conclusion of a ‘object-orientedcategorical syllogism described by @[algorithm of sentence basedobject-oriented categorical syllogism]’ for a major premise” or not. Itshould be noted that Formula. 15A is the main part of the procedure. Ifand when this procedure outlined in Formula. 15A+Formula. 15B+Formula.15C+Formula. 15D is made use of,

[2914] then,

[2915] the ‘consequence’ of a ‘hypothetical propositions’ in theknowledge base

[2916] should be provided with as the ‘Major Premise’ for the procedureoutlined in Formula. 15A,

[2917] and,

[2918] the ‘consequence’ of the

[2919] ‘hypothetical_proposition_(—)

[2920]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,should be regarded as the proposition judged by the procedure outlinedin Formula. 15A, i.e. should be regarded as the ‘Candidate ForConclusion’ in Formula. 15A, For detail, see Formula. 15A, first, andthen, see Formula. 15B+Formula. 15C+Formula. 15D, which are linked toFormula. 15A.

[2921] And if and when the ‘consequence’ of the

[2922] ‘hypothetical_proposition_(—)

[2923]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’, isa hypothetical proposition,

[2924] then, it is recommended that the user of the system should makeuse of the procedure outlined in Formula. 16, which is a quasi-C codewhich outlines the procedure to judge whether a proposition is a“conclusion of a ‘hypothetical syllogism described by @[algorithm ofsentence based object-oriented hypothetical syllogism]’” or not when amajor premise is given.

[2925] When this procedure outlined in Formula. 16 is made use of, then,

[2926] the ‘consequence’ of a ‘hypothetical propositions’ in theknowledge base should be provided with as the ‘Major Premise’ for theprocedure outlined in Formula. 16.

[2927] and,

[2928] the ‘consequence’ of the

[2929] ‘hypothetical_proposition_(—)

[2930]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’,should be regarded as the proposition judged by the procedure outlinedin Formula. 16, i.e.,

[2931] should be regarded as the ‘Candidate For Conclusion’,

[2932] For detail, see Formula. 16.

[2933] Here,

[2934] the procedure with which to use, in the step of the backwardreasoning, the keys described

[2935] in “sentence pattern of physical and/or mathematical rules”and/or in “sentence pattern of function” as a rule,

[2936] and

[2937] the procedure with which, in the step of the backward reasoning,to obtain a rule by using a key described in “sentence pattern ofinstances of solving problems”, both of which are used to implement thefunction in Formula. 4,

[2938] Retrieve_directly_(—)

[2939] ‘Hypothetical_propositions_to_be_used_in_backward_reasoning’();_,

[2940] are the same as

[2941] the procedure with which to use, in the step of the backwardreasoning, the keys described

[2942] in “sentence pattern of physical and/or mathematical rules”and/or in “sentence pattern of function” as a rule,

[2943] and

[2944] the procedure with which, in the step of the backward reasoning,to obtain a rule by using a key described in “sentence pattern ofinstances of solving problems”, both of which I have already disclosedin the present invention to show the way to get ‘hypotheticalpropositions possibly used in forward reasoning’ by using optimized‘descriptors’, optimized ‘names-of-classification-items’,‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’, which Ihave described by now in the present invention,

3.3.11.2.3.4.2.3. ‘Means for Narrowing Down the Target ‘Descriptors’’and ‘Means for Narrowing Down the Target‘Names-of-Classification-Items’’

[2945] If and when the first direct measures described just abovefailed, then another measures should be taken to deal with the case whentoo many ‘hypothetical propositions possibly used in backward reasoning’are retrieved, i.e. when combinatorial explosion occurs.

[2946] That is, if and when the first direct measures failed, it isrecommended that a process according to @[algorithm of narrowing downthe target ‘descriptors’ and/or target ‘names-of-classification-items’]should be carried out. The lexical definition of @[algorithm ofnarrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’], the lexical definition of ‘means fornarrowing down the target ‘descriptors’’, and the lexical definition of‘means for narrowing down the target ‘names-of-classification-items’’have already given in the present invention.

[2947] The procedure described above corresponds to the sentence inFormula. 4, if(combinatorial explosion now and/or in the future stillcan not be avoided) {  Revise_(—) optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—) ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’  by_(—) Carring_out_a_process_according_to_@[algorithm  of narrowing down thetarget  ‘descriptors’ and/or target ‘names-of-classification-items’]_(—) if_necessary_And_Retrieve_again,_(—) ‘Hypothetical_propositions_possibly_used_in_backward_(—) reasoning’_by_using_the_revised_(—) ‘descriptors’,_‘names-of-classification-items’,_(—) ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’( ) ;_.

[2948] The retrieval which is described in this sentence in Formula. 4as,

[2949] And_Retrieve_again,_(—)

[2950]‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_(—)

[2951] by_using_the_revised_(—)

[2952] ‘descriptors’,_‘names-of-classification-items’,_(—)

[2953] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,

[2954] is used as the

[2955]

retrieval to be made in the present steps

, which appears in the lexical definition of @[algorithm of narrowingdown the target ‘descriptors’ and/or target‘names-of-classification-items’].

[2956] And the retrieval which is described in the previous sentence inFormula. 4 as,

[2957] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_by_using_(—)

[2958]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2959] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[2960]and_make_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’();_(—)

[2961] is used as the

[2962]

retrieval made in the preceding step

, which appeared in the lexical definition of @[algorithm of narrowingdown the target ‘descriptors’ and/or target‘names-of-classification-items’].

[2963] That is, roughly speaking, in the process according to@[algorithm of narrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’], first, it is recommended thatreconsideration should be carried out about the optimization madepreviously in the main routine and/or made in the previous step ofopportunistic reasoning; That is, it is recommended that revisedoptimization should be made all over again for ‘descriptors’ and for‘names-of-classification-items’, and then, appropriately revised‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’ should becontrived all over again. Then, it is recommended that a new retrievalof ‘hypothetical propositions possibly used in backward reasoning’ byusing these revised ‘descriptors’, revised‘names-of-classification-items’, revised ‘next-best-natural-nouns’,and/or, revised ‘next-best-natural-verbs’ should be carried out. It isexpected that less number of ‘hypothetical propositions possibly used inforward reasoning’ should be obtained in the revised retrieval, andtherefore, the combinatorial explosion should be avoided.

3.3.11.2.3.4.2.4. ‘Means for Fusing Propositions’

[2964] If the opportunistic reasoning should reach a deadlock in a laterstep of opportunistic reasoning, after the process according to@[algorithm of narrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’] has been carried out in the previousstep of opportunistic reasoning,

[2965] then,

[2966] it is recommended that

[2967] the process carried out according to the @[algorithm of narrowingdown the target ‘descriptors’ and/or target‘names-of-classification-items’]

[2968] should be withdrawn,

[2969] and that process according to @[algorithm of fusing propositions]should be carried out.

[2970] The lexical definition of @[algorithm of fusing propositions] and‘means for fusing propositions’ have already given in the presentinvention.

[2971] Here,

[2972] as the

propositions which should be fused

in the lexical definition of @[algorithm of fusing propositions],

[2973] the ‘presupposition’ of the ‘hypothetical propositions possiblyused in backward reasoning’ that were retrieved in the present step ofbackward reasoning before the process according to @[algorithm ofnarrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’] was carried out

[2974] should be used: In other words, ‘presuppositions’ of the‘hypothetical propositions possibly used in backward reasoning’ thatwere retrieved in the following sentences in Formula. 4, in the presentstep of backward reasoning,’Retrieve_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_by_using_(—)

[2975]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2976] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’();_(—)

[2977] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_(—by)_using_(—)

[2978]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2979] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[2980]and_make_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’();_(—)

[2981] should be used as the

propositions which should be fused

in the lexical definition of @[algorithm of fusing propositions].

[2982] And as the output of the procedure according to @[algorithm offusing propositions],

fused hypothetical propositions to be used in the present step ofbackward reasoning

is obtained.

[2983] The procedure described above corresponds to the sentence inFormula. 4, if(combinatorial explosion now and/or in the future stillcan not be avoided) { Withdraw_the_process_carried_out_according_to_the_(—)  @[algorithm ofnarrowing down the target ‘descriptors’ and/or target ‘names-of-classification-items’]_(—)  and_(—) Carry_out_a_process_according_to_(—)  @[algorithm of fusingpropositions]_to_the_‘presuppositions’_(—) of_the_‘Hypothetical_propositions_possibly_used_in_(—) backward_reasoning’_(—)  if_necessary( ) ;_(—) }

[2984] Exactness of reasoning is often seriously lost when a procedureaccording to @[algorithm of fusing propositions] is carried out. So itis recommended that it should be carried out only in above mentionedsituation. Therefore, it is recommended that the @[algorithm of fusingpropositions] should be carried out only when combinatorial explosioncan not be avoided only by carrying out @[algorithm of narrowing downthe target ‘descriptors’ and/or target ‘names-of-classification-items’]and/or @[algorithm of narrowing down the target ‘descriptors’ and/ortarget ‘names-of-classification-items’] should be withdrawn in a laterstep.

3.3.11.2.3.4.2.5. ‘Means for Broadening out the Target ‘Descriptors’’and ‘Means for Broadening Out the Target‘Names-of-Classification-Items’’

[2985] On the contrary when too less number of ‘hypotheticalpropositions to be used in reasoning of the present step’ are obtained,it is recommended that @[algorithm of broadening out the target‘descriptors’ and/or target ‘names-of-classification-items’] should becarried out. I have already given the lexical definition of @[algorithmof broadening out the target ‘descriptors’ and/or target‘names-of-classification-items’], the lexical definition of ‘means forbroadening out the target ‘descriptors’’, and the lexical definition of‘means for broadening out the target ‘names-of-classification-items’’,in the present invention.

[2986] The retrieval which is described in the sentence in Formula. 4as,

[2987] And_Retrieve_again,_(—)

[2988]‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_(—)

[2989] by_using_the_revised_(—)

[2990] ‘descriptors’,_‘names-of-classification-items’,_(—)

[2991] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,

[2992] is used as the

[2993]

retrieval to be made in the present step

which appears in the lexical definition of @[algorithm of broadening outthe target ‘descriptors’ and/or target ‘names-of-classification-items’].

[2994] And the retrieval which is described in the sentence in Formula.4 as,

[2995]Retrieve_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_by_using_(—)

[2996]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[2997] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’();_(—)

[2998] Retrieve_keys_in_“sentence pattern of instances of solvingproblems”_(—by)_using_(—)

[2999]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[3000] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’,_(—)

[3001]and_make_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’();_(—)

[3002] is used as the

[3003]

retrieval made in the preceding step

, which appears in the lexical definition of @[algorithm of broadeningout the target ‘descriptors’ and/or target‘names-of-classification-items’].

[3004] That is, roughly speaking, in the process according to@[algorithm of broadening out the target ‘descriptors’ and/or target‘names-of-classification-items’], first, it is recommended thatreconsideration should be carried out as for the optimization madepreviously in the main routine and/or made in the previous step ofopportunistic reasoning; That is, it is recommended that revisedoptimization should be made all over again for ‘descriptors’ and for‘names-of-classification-items’, and then, appropriately revised‘next-best-natural-nouns’, and/or, ‘next-best-natural-verbs’ should becontrived all over again. Then, it is recommended that a new retrievalof ‘hypothetical propositions possibly used in backward reasoning’ byusing these revised ‘descriptors’, revised‘names-of-classification-items’, revised ‘next-best-natural-nouns’,and/or, ‘next-best-natural-verbs’ should be carried out.

[3005] As the result, it is expected that more number of ‘hypotheticalpropositions possibly used in backward reasoning’ should be obtained inthe new retrieval, and therefore, the deadlock should be avoided.

[3006] This procedure given above corresponds to the sentence inFormula. 4, if( too less ‘hypothetical propositions possibly used inbackward reasoning’ are retrieved) {  Carry_out_@[algorithm ofbroadening out the target ‘descriptors’  and/or target‘names-of-classification-items’]_(—)  And_Retrieve_again,_(—) ‘Hypothetical_propositions_possibly_used_in_backward_(—)  reasoning’( );_.

3.3.11.2.3.4.2.6. “Means for Picking Up Only the ‘Rules-for-Reasoning’From the “Candidates of the ‘Rules-for-Reasoning’”” Lexical Definitionof @[Algorithm for Eliminating the Noise of the Retrieval of‘Hypothetical Propositions Possibly Used in Backward Reasoning’]

[3007] In cases when a procedure according to @[algorithm of fusingpropositions] has not been carried out, it is recommended that

[3008] whether

[3009] a ‘hypothetical propositions possibly used in backwardreasoning’, which is retrieved in

[3010] the present step of opportunistic reasoning

[3011] is strictly a

[3012] ‘hypothetical propositions to be used in backward reasoning’

[3013] or not,

[3014] should be verified by the user of the system.

[3015] And only ‘hypothetical propositions possibly used in backwardreasoning’ that is judged to be a ‘hypothetical propositions to be usedin backward reasoning’ should be used in the backward reasoning in thepresent step of opportunistic reasoning. The algorithm for thisverification is @[algorithm for eliminating the noise of the retrievalof ‘hypothetical propositions possibly used in backward reasoning’]. Itis recommended that @[algorithm of sentence based object-orientedcategorical syllogism] and/or @[algorithm of sentence basedobject-oriented hypothetical syllogism] should be used to embody@[algorithm for eliminating the noise of the retrieval of ‘hypotheticalpropositions possibly used in backward reasoning’]. And as the basis ofdata on which @[algorithm for eliminating the noise of the retrieval of‘hypothetical propositions possibly used in backward reasoning’] iscarried out, it is recommended that ‘ideal thesaurus’, ‘idealclassification table’, and, ‘hypothetical proposition which is thetarget of the present step of opportunistic reasoning’ should be used.The detail of a recommended embodiment of @[algorithm for eliminatingthe noise of the retrieval of ‘hypothetical propositions possibly usedin backward reasoning’] is outline by using a quasi-C code shown inFormula. 18, for the ideal case in which the hypothetical proposition tobe used in backward reasoning is described exactly according amathematically perfect format.

Lexical Definition of Means for Eliminating the Noise of the Retrievalof Hypothetical Propositions Possibly Used in Backward Reasoning

[3016] @[Algorithm for eliminating the noise of the retrieval of‘hypothetical propositions possibly used in backward reasoning’] and/orsomething that stores the information of it, is a means for eliminatingthe noise of the retrieval of hypothetical propositions possibly used inbackward reasoning.

Lexical Definition of “Means for Picking Up Only the‘Rules-for-Reasoning’ From the “Candidates of the‘Rules-for-Reasoning’”” (Part 2. In the Case of Backward Reasoning)

[3017] Means for eliminating the noise of the retrieval of hypotheticalpropositions possibly used in backward reasoning

[3018] equals to

[3019] “means for picking up only the ‘rules-for-reasoning’ from the“candidates of the ‘rules-for-reasoning’””,

[3020] in the case of backward reasoning.

[3021] It should be noted that how a

[3022] “means for picking up only the ‘rules-for-reasoning’ from the“candidates of the ‘rules-for-reasoning’””

[3023] is carried out in

[3024] each step of opportunistic reasoning

[3025] has already explained in “§ 3.3.11.2.3.0. Overview”.

[3026] As mentioned just before in the lexical definition of @[algorithmfor eliminating the noise of the retrieval of ‘hypothetical propositionspossibly used in backward reasoning’], it is recommended that ‘means forcarrying out sentence based object-oriented categorical syllogism’and/or ‘means for carrying out sentence based object-orientedhypothetical syllogism’ should be used when “means for picking up onlythe ‘rules-for-reasoning’ from the “candidates of the‘rules-for-reasoning’”” is embodied (See FIG. 16). And as the basis ofdata on which “means for picking up only the ‘rules-for-reasoning’ fromthe “candidates of the ‘rules-for-reasoning’”” is carried out, it isrecommended that ‘ideal thesaurus’, ‘ideal classification table’, and,‘hypothetical proposition which is the target of the present step ofopportunistic reasoning’ should be used (See FIG. 16).

[3027] In cases when a procedure according to @[algorithm of fusingpropositions] has been carried out, it is recommended that the

fused hypothetical propositions to be used in the present step ofbackward reasoning

should be used as it is as a ‘hypothetical propositions to be used inbackward reasoning’ without any verification in the present step ofopportunistic reasoning. It is recommended that the verification shouldbe done after the final approximate answer will have been obtained, inthis case.

[3028] These procedure corresponds to the sentence in Formula. 4,

[3029] Unless_@[algorithm of fusingpropositions]_has_not_been_carried_out_(—)

[3030]Choose_the_‘hypothetical_propositions_to_be_used_in_backward_reasoning’_out_of_the

[3031]‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_(—)

[3032] And_else_if_@[algorithm of fusingpropositions]_has_been_carried_out_,

[3033] Use_

fused hypothetical propositions to be used in the present step ofbackward reasoning

_as_it_is

[3034]as_the_‘hypothetical_propositions_to_be_used_in_backward_reasoning’_();_(—)

3.3.11.2.3.4.2.7. @[Algorithm for Determining the HypotheticalProposition Which is to be Used as the Target of the Next Step ofOpportunistic Reasoning] (Part 2: in the Case of Backward Reasoning)

[3035] It is recommended that the hypothetical proposition which is tobe used as the target of the next step of opportunistic reasoning

[3036] should be determined

[3037] on the basis of the

[3038] ‘hypothetical propositions to be used in backward reasoning’,which have bee used in the ‘hypothetical syllogism carried out in thepresent step of opportunistic reasoning’ and on the basis of the

[3039] ‘hypothetical_proposition_(—)

[3040]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’

[3041] by using @[algorithm of sentence based object-orientedcategorical syllogism] and/or @[algorithm of sentence basedobject-oriented hypothetical syllogism].

[3042] This procedure corresponds to the sentence in Formula. 4,

[3043] Determine_the_hypothetical_propositions_(—)

[3044] which_are_to_be_used_(—)

[3045] as_the_target_of_the_next_step_of_opportunistic_reasoning_(—)

[3046] on_the_basis_(—)

[3047]of_the_‘hypothetical_propositions_to_be_used_in_backward_reasoning’_(—)

[3048] in_the_present_step_of_opportunistic_reasoning

[3049] and_(—)

[3050] of_a_hypothetical_proposition_(—)

[3051] which_is_the_target_of_the_present_step_of_backward_reasoning

[3052] of_opportunistic_reasoning_(—)

[3053] by_using_(—)

[3054] @[algorithm of sentence based object-oriented categoricalsyllogism]_and_(—)

[3055] @[algorithm of sentence based object-oriented hypotheticalsyllogism]_backward( );_.

[3056] Detail procedures for the instructions described by thissentences is given in Formula. 5B. This function shown just above is afunction representing @[algorithm for determining the hypotheticalproposition which is to be used as the target of the next step ofopportunistic reasoning] in the case of backward reasoning.

Lexical Definition of @[Algorithm for Determining the HypotheticalProposition Which is to be Used as the Target of the Next Step ofOpportunistic Reasoning] (Part 2: in the Case of Backward Reasoning)

[3057] As I have just mentioned, a recommended procedures for@[algorithm for determining hypothetical propositions which are to beused as the target of the next step of opportunistic reasoning] carriedout in the case of backward reasoning is given in Formula. 5B.

[3058] That is, in the case of backward reasoning, a hypotheticalproposition that is to be used as the target of the next step ofopportunistic reasoning is, in an ideal case, a hypothetical proposition

[3059] whose ‘consequence’ is equal to the ‘presupposition’ of a‘hypothetical proposition to be used in backward reasoning’ that wasused in the reasoning of the present step of opportunistic reasoning,

[3060] and

[3061] whose ‘presupposition’ is equal to the ‘presupposition’ of the

[3062] ‘hypothetical_proposition_(—)

[3063]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’.

[3064] @[Algorithm for determining hypothetical proposition which is tobe used as the target of the next step of opportunistic reasoning] is amechanism of reasoning used in an object-oriented knowledge base systemdisclosed in the present invention.

Lexical Definition of “Means for Determining Hypothetical PropositionsWhich are to be Used as the Target of the Next Step of OpportunisticReasoning”

[3065] @[Algorithm for determining hypothetical propositions which areto be used as the target of the next step of opportunistic reasoning],to which I have given the lexical meaning both in the case of forwardreasoning and in the case of backward reasoning, is equal to

[3066] a “means for determining hypothetical propositions which are tobe used as the target of the next step of opportunistic reasoning”.

[3067] It should be noted that how a

[3068] “means for determining hypothetical propositions which are to beused as the target of the next step of opportunistic reasoning”

[3069] is carried out in

[3070] each step of opportunistic reasoning

[3071] has already explained in “§ 3.3.11.2.3.0. Overview”.

[3072] It is recommended that

[3073] ‘rules-for-reasoning’

[3074] and

[3075] ‘hypothetical_proposition_(—)

[3076]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’shouldbe used,

[3077] as the basis from which

[3078] hypothetical propositions that are to be used as the target ofthe next step of opportunistic reasoning

[3079] are retrieved (See FIG. 17).

[3080] <<Lexical Definition of a hypothetical proposition which is to beused as the target of the next step of opportunistic reasoning>> Ahypothetical proposition determined by @[algorithm for determininghypothetical propositions which are to be used as the target of the nextstep of opportunistic reasoning] is a hypothetical proposition which isto be used as the target of the next step of opportunistic reasoning.

[3081] As mentioned before, the problem is judged to be solved when atleast one hypothetical proposition that is to be used as the target ofthe next step of opportunistic reasoning, is judged to be trivial.

[3082] In the case when the ‘presupposition’ is an empty set, theproblem is judged to be solved when the ‘presupposition’ of the majorpremise of the hypothetical syllogism carried out in the next step is awell known theorem and/or axiom.

[3083] This corresponds to a sentence in Formula. 2, if( Problem issolved ) {  break ;_(—) }.

[3084] In the case when there is a step of forward and/or backwardreasoning in which the exactness is lost by for example the execution of@[algorithm of fusing propositions], it is recommended that all thesteps of the theorem proving should be reconstructed by a series of moreexact steps of opportunistic reasoning all over again as a verificationfor the sake of exactness. This procedure corresponds to

[3085] /* Verification of the Result of above Opportunistic Reasoning */

[3086]Re-prove_all_the_steps_of_the_proof_by_a_series_of_steps_of_more_exact_opportunistic_reasoning();_in Formula. 2.

2.3.12. Recommended Way to Use and/or Make an Object-Oriented KnowledgeBase System Disclosed in the Present Invention

[3087] In the present invention,

[3088] I regard

[3089] a procedure

[3090] with which

[3091] the knowledge described in a document using natural language

[3092] is

[3093] translated into

[3094] sentences described by using systematic data structures, such as,“sentence pattern of association”, “sentence pattern of ‘idealthesaurus’”, “sentence pattern of implementation of names ofalgorithms-of-processes”, “sentence pattern of classification”,“sentence pattern of physical and/or mathematical rules”, “sentencepattern of function”, and “sentence pattern of definition of object”

[3095] and

[3096] as the basis of which

[3097] background knowledge, understanding of consistency in grammaticalrestriction such as gender and number, linking of pieces of descriptionsin the document are used,

[3098] as,

[3099] a procedure with which the essence of a sentence written innatural language (e.g. title and synopsis of a scientific paper) islogically understood.

Lexical Definition of ‘Knowledge Molecule’

[3100] I regard thus formalized sentence describing the element of aknowledge as the fundamental unit of knowledge, and call it a ‘knowledgemolecule’. This ‘knowledge molecule’ can be readily used as a buildingblock of body of association and/or inference.

[3101] A sentence having a data structure defined in the presentinvention is suitable to be used to describe fundamental unit ofproposition (i.e. to describe a building block) in the ‘propositionalrepresentation theory’ in cognitive psychology ({circle over(∘)}“Gurafikku Ninchi shinrigaku”, p.174).

[3102] I claim that a ‘knowledge molecule’ is suitable to be used as akey of data stored in an object-oriented knowledge base system disclosedin the present invention.

[3103] The systematic data structure includes the negative form of abovementioned fundamental sentence patterns. In other words, a negativesentence containing ‘do not’ and/or ‘is not’, etc., described in abovementioned fundamental sentence patterns, may be used as a fact and/or asa rule. It is recommended that a predicate logic should be made full useof. For example, it is recommended that a rule to lead contraposition ofa sentence from other sentence should made full use of, and should bedescribed using a sentence in “sentence pattern of physical and/ormathematical rules” and/or in “sentence pattern of function”, if anegative sentence is used as a rule and/or as a fact.

[3104] It is recommended that the synopsis, the section of introduction,the theory, and the experiment of a scientific paper and/or itsequivalent, should be translated into ‘knowledge molecules’ by aknowledge engineer, when a scientific information is to be stored in anobject-oriented knowledge base system disclosed in the presentinvention.

Industrial Applicability 3.4. CAD for Coding of Computer Programs

[3105] This time, I regard subroutines and programs as a function thatconnects the input to the output. In other words, it is recommended thata sentence described in “sentence pattern of function” should be used asan interfaces of subroutines and/or as an interfaces of programs.Following discussions will show that this is equivalent to the standpoint of view in which an object-oriented knowledge base systemdisclosed in the present invention is regarded as an CAD (Computer AidedDesign) that aids the coding of computer program.

[3106] For example, let us assume that we want to code a computerprogram that gives the output, ‘C’, if and when one gives the computerprogram an input ‘A’:.

[3107] Let us assume that if we succeed in retrieving

[3108] a subroutine ‘(1)’, which gives output ‘B’ if and when one gives‘(1)’ an input ‘A’,

[3109] and

[3110] a subroutine ‘(2)’, which gives output ‘C’ if and when one gives‘(2)’ an input is ‘B’, from an object-oriented knowledge base systemdisclosed in the present invention: That is, in this situation, two keysdescribed in “sentence pattern of function”, _FUNCTION_subroutine‘(1)’_translate_INPUT_‘A_into_OUTPUT_‘B’;_(——)FUNCTION_subroutine‘(2)’_translate_INPUT_‘B’_into_OUTPUT_‘C’;_are retrieved from anobject-oriented knowledge base system disclosed in the presentinvention.

[3111] As mentioned before, a sentence described in “sentence pattern offunction” is called a ‘means for describing a function used as a rule’.It should be noted that here, the name of a subroutine is used as thename of the function used in a ‘means for describing a function used asa rule’(See FIG. 7).

[3112] If, the subroutine ‘(1)’ and the subroutine ‘(2)’ are arranged inthis order in a computer program, then, this computer program isexpected to work well as the computer program that we wanted to code.

[3113] I claim that this simple idea is of great help of coding acomputer program. For example, if and when too many keys possibly to beused as a subroutine are retrieved during these retrieval, it isrecommended that @[algorithm of narrowing down the target ‘descriptors’and/or target ‘names-of-classification-items’] and/or @[algorithm offusing propositions] should be used to prevent combinatorial explosion.And as another example, if and when too less keys possibly used as asubroutine are retrieved, then, it is recommended that @[algorithm ofbroadening out the target ‘descriptors’ and/or target‘names-of-classification-items’] should be carried out.

[3114] There exists usually a nesting structure ({circle over (∘)}“Cgengo puroguramingu” p.51) in a usual code of computer programs.

[3115] If, in an object-oriented knowledge base system disclosed in thepresent invention, many subroutines are stored as a data that has a keysdescribed in the “sentence pattern of function”, then theobject-oriented knowledge base system can be used as a CAD for coding acomputer programs.

[3116] When a parallel processing computer program is coded, the codingtechnique of single task computer program is rehashed and used to solvethe problem. The fundamental strategy is to regard the computer used asthe platform of a multi task program as a finite state machine.

[3117] We discuss without losing the generality, about an coding of anapplication program ‘P_(A)’, which includes three parallel processes,‘X1’, ‘X2’, and ‘X3’. If the number of state of process ‘X1’ equals to‘N1’, the number of state of process ‘X2’ equals to ‘N2’, and the numberof states of process ‘X3’ equals to ‘N3’, then application program‘P_(A)’ may have up to N1×N2×N3 states at maximum.

[3118] Each ‘state’ distinguished in usual flow charts is an example ofa unit with which to count such number of states of a process, and/or ofan application program.

[3119] Thus, during the job of application program ‘P_(A)’, the computercan be regarded as a finite state machine which may have up to N1×N2×N3states.

[3120] An algorithm of a finite states machine can be constructed bymeans of top-down task analysis method ({circle over (∘)}“C gengopuroguramingu” p.61), and therefore, can be coded as if it were a singletask program by using an object-oriented knowledge base system disclosedin the present invention.

[3121] In the present invention, I disclose a programming techniquebased on structured coding technique in which objects are defined andmade full use of. That is, I claim that if “sentence pattern offunction” and “sentence pattern of definition of object” aresystematically used, then, object-oriented programming technique({circle over (∘)}“C++ nyumon kani puroguramingu gaido”) and structuredcoding technique can be unified and used systematically in a style ofcoding of a computer programming.

[3122] If a source code is written in object-oriented style, then, aprogrammer can find out easily a function (i.e. a subroutine) he wantsout of codes written by him and/or out of codes written by otherprogrammers. If and when very big computer program is coded by manyprogrammers, then, object-oriented style is a recommended style ofcoding. That is, I claim that object-oriented programming is essentiallya method in which a source code is regarded as an object-orienteddatabase, and as the result, the retrieval of a necessary function ismade easier. A code of a computer program can be stored in an‘object-oriented knowledge base’ disclosed in the present invention, ifa function (i.e. a subroutine) is recorded as a data whose interface isrecorded using a sentence in “sentence pattern of function” and is usedas the key of the data.

[3123] It is recommended that even a multi process computer programshould be coded on the basis of the structured coding method accordingto the top down task analysis method if an object oriented knowledgebase system disclosed in the present invention is used as a CAD forcoding of the program.

[3124] An object-oriented knowledge base system disclosed in the presentinvention has an advantage characteristic to an object-oriented databasebecause it uses “sentence pattern of definition of object” and “sentencepattern of ‘ideal thesaurus’”. In addition, the present object-orientedknowledge base system has an advantage characteristic to a knowledgebase because it uses @[algorithm of sentence based object-orientedcategorical syllogism] and @[algorithm of sentence based object-orientedhypothetical syllogism].

[3125] A detailed example in which the present object-oriented knowledgebase system will be shown later in the present invention.

3.4.1. Example in Which a Knowledge Base System is Applied to aPractical Case of Computer Programming

[3126] Next, I will show a practical example in which an object orientedknowledge base system disclosed in the present invention is used as aCAD that assists coding of computer programs.

[3127] Let me show an example in which a school teacher has made up hismind to code a computer program to calculate the average grade in hisclass on the basis of the examination results of his students by usingan object-oriented knowledge base system of the present invention.

[3128] According to the top-down task analysis method, it is recommendedthat the school teacher should clearly define the input and the outputof the top task. Here, a top task means the main routine of the computerprogram to be coded. In the present case, the input is the number of hisstudents (let it be 30 without losing the generality), and the grade ofeach student. The output is the average grade in his class.

[3129] As the term ‘average’ is a mathematically very well defined term,it is recommended that a backward reasoning should be carried out in thefirst step of opportunistic reasoning. Let us assume that the teacherconsults an ‘ideal thesaurus’ and finds out that the term ‘_(average)_’is registered in the ideal thesaurus. And let us assume that the teacherconsults keys described in “sentence pattern of ‘ideal thesaurus’” inthe thesaurus further, and finds out that the ‘ideal thesaurus’ stores‘_(arithmetic average)_’ as a narrower descriptor of ‘_(average)_’. Letus assume that the teacher consult an ‘ideal dictionary’ of theobject-oriented knowledge base system, and found that the ‘_(arithmeticaverage)_’ rather than ‘_(average)_’ expresses more precisely his idea.Thus, in a word, I have assumed by now that the teacher has succeeded insearching a descriptor that expresses well the content of the teacher'sidea.

[3130] Then, it is recommended that, among the keys described in“sentence pattern of function”,

[3131] FUNCTION_***_translate_INPUT_***_into_OUTPUT_***;_,

[3132] stored in the ‘object-oriented knowledge base’,

[3133] the teacher should retrieve the keys

[3134] whose field below ‘_into_OUTPUT_’contains ‘_(arithmeticaverage)_’ and/or its broader ‘descriptors’. As I described before, asentence described in “sentence pattern of function” is called a ‘meansfor describing a function used as a rule’ in the present invention.Here, it should be noted the field below ‘_FUNCTION_’ (i.e. the name ofthe function used in ‘means for describing a function used as a rule’)should contain the name of a subroutine and/or contains the name of acomputer program in this case (See FIG. 7).

[3135] Let us assume that, among the keys described in the “sentencepattern of function” which are thus retrieved, there exists in thepresent example a key whose field below_translate_INPUT_contains‘_(array of constants)_’. This means that the school teacher succeededin retrieving a subroutine which transforms an ‘array of constants’ into‘an arithmetic average’.

[3136] Let us assume that the teacher consults the ‘ideal thesaurus’and, and he found that ‘grade’ is a kind of a ‘constant’. Then, let usassume that the teacher begins to find a method with which to transform‘constants’ into an ‘array of constants’. Then, it is recommended thatthe teacher should try to retrieves a key described in “sentence patternof function” whose field below ‘_translate_INPUT_’ contains a,‘_(constant)_’ and/or its broader ‘descriptors’ and whose field below‘into_OUTPUT_’ contains, ‘_(array of constants)_’ and/or its broader‘descriptors’. In other words, the school teacher retrieves a subroutinein which an array is initialized by using constants. Let us assume inthe present example that such subroutine is retrieved successfully.

[3137] Up to now, we have assumed that the school teacher has obtained

[3138] 1) a subroutine which initializes array with grades of students,and,

[3139] 2) a subroutine that gives arithmetic average for an array ofconstants.

[3140] If these subroutines are arranged in this order, then, a backboneof the computer program is obtained. And if and when the details areimplemented, then, the coding of the computer program completes.

[3141] During such retrievals, it is recommended that @[algorithm ofnarrowing down the target ‘descriptors’ and/or target‘names-of-classification-items’] and/or @[algorithm of fusingpropositions] should be carried out if and when too many subroutines areretrieved. And it is also recommended that @[algorithm of broadening outthe target ‘descriptors’ and/or target ‘names-of-classification-items’]should be carried out if necessary when too less subroutines areretrieved. And if the school teacher dose not think of a usable‘descriptor’ by himself, then, it is recommended that he should use@[algorithm of making a list of ‘descriptors’ ranked in order of hitfrequency] to search ‘descriptors’ associated with a natural worddescribing the idea of the teacher.

[3142] In the present case, it is recommended that the maker of thecontents of the knowledge base should make a rich ‘ideal thesaurus’ inwhich ‘descriptors’ representing varieties of types of the variablesused as operands of subroutines, such as ‘constants’, ‘array ofconstants’, ‘variables’, ‘array of variables’, ‘characters’, and/or‘array of characters’, ‘name of other function’ etc.

3.5. On-Line Manual and/or On-Line Help of Machines

[3143] Handling of contemporary machineries such as word processors,personal computers, and home electric appliances with digitaltechnologies has become more and more complicated. Here, a finite statemachines and/or finite automaton is regarded as a ‘machine’.

[3144] When software and/or firmware is executed on a computer, thecomputer is working as a finite state machine, because the capacity ofstorage media for digital computers is finite.

[3145] When groupware is used on a computer network system, the computernetwork system is working a finite state machine, because the capacityof storage media connected to the computer network is finite.

[3146] In many cases, an user of a machine must carry out laborious taskto make the machine do the motion he wants: The user must consult themanual of the machine, must find the relevant unit operations describedseparately in different places of the manual, must integrate them in hishead, to compose the total process for the operation, and must carrythem out the total process on the machine. Only after the proceduredescribed above, the machine works in a desirable way. That is, onlyafter a correct operation is given, the machine makes a series of motionstarting from its stopping state and ending with the state of the motionof the machine that the operator desired.

[3147] An object-oriented knowledge base system disclosed in the presentinvention provides a method that is used as aids to this laborious task.For example, an user of a machine ‘X’ wants the machine ‘X’ to changeits state from its stopping state ‘A’ to a working state ‘D’. The state‘D’ is the state of the motion of the machine ‘X’ which the operatordesires.

[3148] Let us assume that three keys in described in “sentence patternof function”

[3149] _FUNCTION_ unit operation ‘(1)’ described on ‘page(1)’ of theon-line manual _translate_INPUT_ stopping state ‘A’ _into_OUTPUT_working state ‘B’,

[3150] _FUNCTION_ unit operation ‘(2)’ described on ‘page(2)’ of theon-line manual _translate_INPUT_ working state ‘B’ _into_OUT_ workingstate ‘C’,

[3151] and

[3152] _FUNCTION_ unit operation ‘(3)’ described on ‘page(3)’ of theon-line manual _translate_INPUT_ working state ‘C’ into_OUT_ workingstate ‘D’,

[3153] should be retrieved as a result of several steps of opportunisticreasoning on an object-oriented knowledge base system disclosed in thepresent invention, which ‘prove’ ‘D’ by using ‘A’.

[3154] If, unit operation ‘(1)’, unit operation ‘(2)’, and unitoperation ‘(3)’ arranged in this order in a sentence,

[3155] then,

[3156] the sentence can be used as the procedure with which the operatorlets the machine working in state ‘D’.

[3157] In this case too, it is recommended that @[algorithm of narrowingdown the target ‘descriptors’ and/or target‘names-of-classification-items’] and/or @[algorithm of fusingpropositions] should be carried out if and when too many unit operationsare retrieved, and that @[algorithm of broadening out the target‘descriptors’ and/or target ‘names-of-classification-items’] should becarried out if and when too less unit operations are retrieved. And itis recommended that @[algorithm of sentence based object-orientedcategorical syllogism] and @[algorithm of sentence based object-orientedhypothetical syllogism] should be used in an opportunistic reasoningwith which to get the procedure for the correct operation.

[3158] As I described before, a sentence described in “sentence patternof function” is called a ‘means for describing a function used as arule’ in the present invention. Here, it should be noted that the nameof an unit operation described in a manual is described in the fieldafter ‘_FUNCTION_’. That is, the name of an unit operation described ina manual is used as the name of the function used in a ‘means fordescribing a function used as a rule’ (See FIG. 7). It is recommendedthat an on-line manual embodied on the basis of an object-orientedknowledge base system disclosed in the present invention should satisfyfollowing conditions:

[3159] 1) In an on-line manual of a machine, each unit operation that isdistinguishable from other unit operations, should have a name. It isrecommended that the maker of the on-line manual should give the name inan explicit and systematic way. In a word, it is recommended that thenames should be regarded as a ‘names-of-classification-items’ and shouldbe registered in a ‘classification table’ for the on-line manual. Anexample of such a ‘classification table’ for a Japanese word processorhas already been shown in Formula. 6.

[3160] Such a ‘classification table’ can be regarded as a mathematicallysophisticated ‘table of contents’ of an on-line manual revised so as tobe readily used in an object-oriented knowledge base system disclosed inthe present invention.

[3161] 2) In an on-line manual of a machine, it is recommended that eachdistinguishable working state should be explicitly and systematicallynamed by the maker of the on-line manual. These named should be regardedas ‘descriptors’ and should be registered in an ‘ideal thesaurus’.

[3162] This can be expressed more precisely as follows:

[3163] If an object-oriented knowledge base system disclosed in thepresent invention is to be used as an on-line manual,

[3164] then

[3165] 1) it is recommended that each name of unit operation should beregarded as a name of a function. In other words, it is recommended thatthe name of unit operation should be described as a sentence describedin “sentence pattern of function”, and should be used as a key of theobject-oriented knowledge base system disclosed in the presentinvention,

[3166] and the name of unit operation should be registered as a‘name-of-classification-item’ in a ‘classification table’, in whichhigher class ‘name-of-classification-items’ and lower class‘name-of-classification-items’ are defined,

[3167] 2) it is also recommended that all the working states of that canbe used as operands of the function mentioned just above, should benamed systematically and explicitly, and registered in an ‘idealthesaurus’, in which broader ‘descriptors’ and narrower ‘descriptors’are given,

[3168] and,

[3169] 3) It is recommended that a set of data comprising sentencesdescribed in “sentence pattern of association” should be given

[3170] to be used as the basis on which @[algorithm of making a list of‘descriptors’ ranked in order of hit frequency] should be carried out toprovide the user an appropriate ‘descriptor’ associated with an naturalword given by the user, and/or

[3171] to be used as the basis on which @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] shouldbe carried out to provide the user an appropriate‘name-of-classification-item’ associated with a natural word given bythe user.

[3172] I discuss here the manual of Japanese word processor as anexample of a manual of a machine.

[3173] Let me discuss the ‘ideal thesaurus’ and the ‘classificationtable’ to be used in the on-line manual, first:

[3174] When “kanji-kana mixed sentence” is regarded as an ‘object’,then, it is recommended that either ‘kanji’ and/or ‘kana’ should beregarded as an ‘individual variable’ of this ‘object’. And it isrecommended that ‘to convert half-sized alphanumeric characters tokanji’ should be regarded as a ‘name-of-classification-item’,representing a function that maps ‘kanji’. And it is also recommendedthat ‘to convert half-sized alphanumeric characters to kana’ should beregarded as a ‘name-of-classification-item’, representing a functionthat maps ‘kana’. And it is recommended that both of these‘name-of-classification-items’ should be registered in the‘classification table’ of Formula. 6. It is recommended that ‘kanji-kanamixed sentence’ should be registered in an ‘ideal thesaurus’ as a‘descriptor’. And ‘to input kanji characters’ in a classification tableis a higher class ‘names-of-classification-items’ of ‘to converthalf-sized alphanumeric characters to kanji’. It should be recommendedthat ‘kanji-kana mixed sentence’ should be regarded as a narrower‘descriptor’ of ‘sentence’.

[3175] Such an ‘ideal thesaurus’ can be regarded as a mathematicallysophisticated ‘index’ of a manual revised so as to be readily used in anobject-oriented knowledge base system disclosed in the presentinvention.

[3176] A user of a machine often expresses a moving state of the machinein various words (the problem of polysemous words). Therefore, it isrecommended that an author of a manual should prepare enough number ofkeys in the “sentence pattern of association” so as to the @[algorithmof making a list of ‘descriptors’ ranked in order of hit frequency] beused readily so as to let the user know an appropriate ‘descriptor’associated with the various words of a user.

[3177] It is extremely important that the maker of the manual shouldgive an explicit name to the machine's stopping state, after which theinitial moving state of the machine just after the power is turned onfollows. The operation with which the machine is let to be in thedesired moving state can be readily retrieved only when an explicit nameof the stopping state is given by the maker of the manual.

[3178] In each item of a manual explaining unit operation, it isrecommended that not only the maker of the manual should give anexplicit name to the machine's moving state in which the machine isafter the unit operation is carried out, but also the maker of themanual should give an explicit name to the machine's moving state inwhich the machine should be in, before the unit operation is carried outby the user.

[3179] It is recommended that both of these should be practiced withoutfail, if an on-line manual is to be embodied using an object-orientedknowledge base system disclosed in the present invention, and eachexplanation of unit operation is stored as a key described in the“sentence pattern of function”. It is recommended that sufficient numberof these keys described in “sentence pattern of function” should beregistered, if one wants provide an on-line manual of a machine.

[3180] In many conventional manuals, only the moving state of themachine after the operation is described, and the moving state that themachine should be in before the operation is not described explicitly.This and the problem of polysemous words causes the necessity of hunchand experience in operating machines even when using manuals, and madeit impossible to integrate a series of unit operations only by reasoningon the basis of the descriptions in manuals.

[3181] As for the problem of nesting, it is recommended that after thebackbone of the total operation has constructed, nesting in operationshould be added if nesting is necessary and/or beneficial in movingmachines.

3.6. Example in Which an Object-Oriented Knowledge Base System Disclosedin the Present Invention is Applied to a Practical Case in Solving aProblem in Metallurgical Physics

[3182] A metallurgical physicists call a thermal migration of aluminumatoms on a surface of a bulk aluminum metal a ‘surface diffusion ofaluminum on a surface of a bulk aluminum metal’. A surface diffusion isa kind of equation of motion, and it is well known that it can be usedto describes the “mass transformation on the of a surface of a bulkaluminum caused by the surface diffusion”. Metallurgical physicistsknows that rough surface of a bulk aluminum metal can be made smooth, ifthe bulk metal is annealed at high temperatures. Metallurgicalphysicists knows this surface annealing is caused by the surfacediffusion caused by the surface tension and that change of the surfaceroughness can be described as a function of time, by using an surfacediffusion equation, if the temperature and the initial roughness of thesurface is known.

[3183] But it was shown in 1997, by a metallurgical physicist S. Okude,and et. al. that a surface diffusion equation breaks is well definedonly if the length that characterizes the area is much more than 60 Å(less than 60×10⁻¹⁰ meter) by using a computer simulation. ({circle over(∘)}“Two dimensional stochastic Monte Carlo simulation for smoothing ofscratched surface of aluminum thin film at 773K”)

[3184] Here, I show an example in which a problem in metallurgicalphysicists is solved (i.e. a lemma in metallurgical physicists isproved) on the basis of an opportunistic reasoning model by using@[algorithm of sentence based object-oriented categorical syllogism] andby @[algorithm of sentence based object-oriented hypotheticalsyllogism].

(Problem)

[3185] Under the premise

[3186] that the migration of aluminum atoms (Al atoms) adsorbed on thesurface of metal aluminum at 500° C. plays a dominant role in thesurface diffusion of metal aluminum at 500° C.,

[3187] prove the lemma described as a hypothetical proposition,

[3188] prove the lemma,

[3189] 4) If the width of a groove with aspect ratio (depth/width) 1 onthe surface of metal aluminum at 500° C. is 32 Å, then, surfacediffusion equation breaks down near the groove.

[3190] To prove this lemma, use the following rules and or facts,described as a hypothetical proposition and/as a categoricalproposition;

[3191] 1) The mean free path of the migration of Al atoms adsorbed onthe surface of metal aluminum at 500° C. is 60 Å,

[3192] 2) According to Einstein's equation, if the typical size of asurface structure is less than the mean free path, surface diffusionconstant can not be well defined near the surface structure,

[3193] 3) If surface diffusion constant can not be well defined, then,surface diffusion equation breaks down,

[3194] 7) A groove with aspect ratio (depth/width) 1 on the surface ofmetal aluminum at 500° C. is a kind of surface structure,

[3195] 8) The width of a groove with aspect ratio (depth/width) 1 on thesurface of metal aluminum at 500° C. is a kind of the typical size of asurface structure, and,

[3196] 10) If it is 32 Å, then it is less than 60 Å.

(Answer to the Problem on the Basis of Natural Language)

[3197] First I show an example in which the problem is solved bycarrying out backward reasoning as follows;

[3198] It should be noted that lemma ‘4)’ is the proposition to befinally proved.

[3199] It is recommended that the hypothetical proposition ‘4)’ shouldbe used as the hypothetical proposition which is the target of the firststep of opportunistic reasoning.

[3200] Here, it should be noted that,

[3201] if

[3202] a hypothetical proposition,

[3203] 5) If the width of a groove with aspect ratio (depth/width) 1 onthe surface of metal aluminum at 500° C. is 32 Å, then, surfacediffusion constant can not be well defined near the groove,

[3204] is proved to be true,

[3205] then,

[3206] the hypothetical proposition ‘4)’ can be proved to be true, byinserting the hypothetical proposition ‘3)’ into the hypotheticalproposition ‘5)’ on the basis of the usual syllogism (i.e. “if ‘a→b’ and‘b→c’, then ‘a→c’”), in a backward reasoning in the present step ofopportunistic reasoning.

[3207] Proposition ‘4)’ is‘the_Proposition_to_be_finally_proven_by_using_the_system’ in Formula.2, but in the present first step of opportunistic reasoning, Proposition4) is regarded as the

[3208] ‘hypothetical proposition that is the target of the present stepof opportunistic reasoning’.

[3209] And the hypothetical proposition ‘3)’ is a “hypotheticalproposition to be used in backward reasoning” in the first step ofopportunistic reasoning (See Formula. 4). And the hypotheticalproposition ‘5)’ is the “hypothetical proposition which is to be used asthe target of the next step of opportunistic reasoning” (See Formula.4).

[3210] In the second step of opportunistic reasoning, in a similar way,backward reasoning is carried out. It should be noted here that, thehypothetical proposition ‘5)’ should be used as the hypotheticalproposition which is the target of the second step of opportunisticreasoning. Here, it should be noted that,

[3211] if

[3212] a hypothetical proposition,

[3213] 6) If the width of a groove with aspect ratio (depth/width) 1 onthe surface of metal aluminum at 500° C. is 32 Å, then the typical sizeof a surface structure is less than the mean free path (of the migrationof Al atoms adsorbed on the surface of metal aluminum at 500° C.),

[3214] is proved to be true,

[3215] then,

[3216] under previously described premise, proposition ‘5)’ can beproved to be true by inserting the hypothetical proposition ‘6)’ and thecategorical proposition ‘7)’ into the hypothetical proposition ‘2)’ onthe basis of the usual syllogism (i.e. “if ‘a→b’ and ‘b→c’, then ‘a→c’”)and on the basis of @[algorithm of sentence based object-orientedhypothetical syllogism].

[3217] The categorical proposition, ‘7)’ is used as the minor premise of@[algorithm of sentence based object-oriented categorical syllogism],which leads,

[3218] the, categorical proposition,

[3219] “the width of a groove with aspect ratio (depth/width) 1 on thesurface of metal aluminum at 500° C. is 32 Å” (i.e. consequence of‘5)’),

[3220] from

[3221] the categorical proposition,

[3222] “the typical size of a surface structure is less than the meanfree path (of the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C.). (i.e. conclusion of ‘6)’).

[3223] The hypothetical proposition ‘2)’ is the “hypotheticalproposition to be used in backward reasoning” (see Formula. 4). And thehypothetical proposition ‘6)’ is the “hypothetical proposition which isto be used as the target of the next step of opportunistic reasoning”(see Formula. 4).

[3224] Therefore, proposition ‘5)’ can be proved to be true ifproposition ‘6)’ is proved to be true. In the next step, I must try toprove ‘6)’.

[3225] In the third step of opportunistic reasoning, in a similar way,backward reasoning is carried out. It should be noted here that, thehypothetical proposition ‘6)’ should be used as the hypotheticalproposition which is the target of the third step of opportunisticreasoning. Here, it should be noted that,

[3226] if a hypothetical proposition,

[3227] 9) If the typical size of a surface structure is 32 Å, then thetypical size of a surface structure is less than the mean free path (ofthe migration of Al atoms adsorbed on the surface of metal aluminum at500° C.),

[3228] is proved to be true, then proposition ‘6)’ can be proved to betrue by inserting hypothetical proposition ‘8)’ into ‘9)’ on the basisof the usual syllogism (i.e. “if ‘a→b’ and ‘b→c’, then ‘a→c’”), by usinga backward reasoning.

[3229] The hypothetical proposition ‘8)’ is a “hypothetical propositionto be used in backward reasoning” (see Formula. 4). And the hypotheticalproposition ‘9)’ is the “hypothetical proposition which is to be used asthe target of the next step of opportunistic reasoning” in Formula. 4.

[3230] Therefore, proposition 6) can be proved to be true if proposition9) is proved to be true. In the following discussion, I must try toprove 9).

[3231] In the fourth step of an opportunistic reasoning, in a similarway, backward reasoning is carried out. It should be noted here that,the hypothetical proposition ‘9)’ should be used as the hypotheticalproposition which is the target of the fourth step of opportunisticreasoning. Here, it should be noted that,

[3232] if

[3233] a proposition,

[3234] 11) If the typical size of a surface structure is less than 60 Å,then the typical size of a surface structure is less than the mean freepath (of the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C.),

[3235] is proved to be true,

[3236] then,

[3237] proposition ‘9)’ can be proved to be true by inserting thehypothetical proposition ‘10)’ into the hypothetical proposition ‘11)’on the basis of usual syllogism (i.e. “if ‘a→b’ and ‘b→c’, then‘a→c’”).

[3238] The hypothetical proposition ‘10)’ is the “hypotheticalproposition to be used in backward reasoning” (see Formula. 4). And thehypothetical proposition ‘11)’ is the “hypothetical proposition which isto be used as the target of the next step of opportunistic reasoning” inFormula. 4.

[3239] Therefore, proposition ‘9)’ can be proved to be true ifproposition ‘11)’ is proved to be true. In the following discussion, Imust try to prove ‘11)’.

[3240] In the fifth step of an opportunistic reasoning, in a similarway, it should be noted that, the hypothetical proposition ‘11)’ shouldbe used as the hypothetical proposition which is the target of the thirdstep of opportunistic reasoning. Here, it should be noted that,

[3241] if

[3242] a proposition ‘1)’ is inserted into proposition ‘11)’,

[3243] then,

[3244] a proposition ‘12)’ as,

[3245] 12) If the typical size of a surface structure is less than themean free path (of the migration of Al atoms adsorbed on the surface ofmetal aluminum at 500° C.), then the typical size of a surface structureis less than the mean free path (of the migration of Al atoms adsorbedon the surface of metal aluminum at 500° C.), is obtained.

[3246] This hypothetical proposition, ‘12)’ is trivial. Thus hire, Ijudge that the hypothetical proposition, ‘4)’ has proven to be truth.

[3247] This corresponds to the sentence, if( Problem is solved ) { break ;_(—) },

[3248] in Formula. 2.

[3249] From now on, the same problem will be solved on the basis ofsystematic data structures. It is recommended that, first, the maker ofthe contents of an object-oriented knowledge base system disclosed inthe present invention should reformulate the problem itself on the basisof systematic data structures, using “sentences that store data used asrules”, as follows:

[3250] Under the premise

[3251] that the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C. plays a dominant role in the surface diffusion ofmetal aluminum at 500° C., prove the hypothetical proposition

[3252] 4):

[3253] _RULE_Answer to the problem

[3254] _states_if(

[3255] _S=_the width of a groove with aspect ratio (depth/width) 1 onthe surface of metal aluminum at 500° C. _V=_is_C=_(—)32 Å;_)then{

[3256] _S=_surface diffusion equation _V=_is not valid near the groovewith aspect ratio (depth/width) 1 on the surface of metal aluminum at500° C.

[3257] };_.

[3258] And it is recommended that rules and to prove this lemma shouldbe formalized, too, as follows: /* Fact described by using “sentencepattern of definition of object”  */ 1): _OBJECT_(—)  Al atoms adsorbedon the surface of metal aluminum at 500° C._ <<Noun_KW>> _=_(—) _(Aluminum)_,_(Metal Surface)_,_(Adsorbed Atom)_. have_VARIABLES  themean free path of the migration_ <<Noun_KW>> _=_(— —)(Migration)_,_(MeanFree Path)_. which_is  60 Å, /* Rules described by using “sentencepattern of physical and/or mathematical rules”  */ 2): _RULE_(—) Einstein's Equation_ <<Noun_KW>> _=_(— —)(Einstein's Equation)_._states_if(  _S=_the typical size of a surface structure_ <<Noun_KW>>_=_(— —)(Surface  Structure)_,_(Size)_. _V=_is _C=_less than the meanfree path_ <<Noun_KW>> _=_(—) _(Mean  Free Path)_;_. )then{  _S=_surfacediffusion constant_<< Noun_KW >> _=_(— —)(Surface)_,_(DiffusionCoefficient)_.  _V=_can not be well defined near the surface structure_<<Noun_KW>> _=_(— —)(Surface  Structure)_._

Verb_KW

_=_(— —){Break Down}_. ;_} ;_(—) 3): _RULE_(—)  Self Evident Rule_states_if(  _S=_surface diffusion coefficient_ <<Noun_KW>>_=_(— —)(Surface)_,_(Diffusion  Coefficient)_. _V=_has no meaning _

Verb_KW

_=_(— —){Break Down}_;_(—) )then{  _S=_surface diffusion equation_<<Noun_KW>> _=_(— —)(Surface)_,_(—)  (Diffusion Equation)_.  _V=_is notvalid _

Verb_KW

_=_(— —){Break Down}_. ;_(—) } ;_(—) 10):  _RULE_arithmetic_states_if(_S=_length_V=_is_C=_32 Å ;_(—) )then{  _S=_length _V=_is_C=_less than 60 Å ;_(—) } ;_(—) /* Facts described by using “sentencepattern of ‘ideal thesaurus’”  */ 7): _NT_(—)  A groove with aspectratio (depth/width) 1 on the surface of metal aluminum at 500° C.  _<<Noun_KW>> _=_(— —)(Aluminum)_,_(Metal Surface)_,_(Surface Structure)_._is_a_kind_of_BT_(—)  surface structure_ <<Noun_KW>> _=_(— —)(SurfaceStructure)_. 8): _NT_(—)  The width of a groove with aspect ratio(depth/width) 1 on the surface of metal aluminum  at 500° C._<<Noun_KW>> _=_(— —)(Aluminum)_,_(Metal Surface)_,_(—)  (SurfaceStructure)_._is_a_kind_of_BT_(—)  the typical size of a surfacestructure_ << Noun_KW >> _=_(— —)(Surface Structure)_,_(Size)_. /*Several “sentences that store data providing the ability of association” * described in “sentence pattern of association”.  */ *):_Association_(—) a groove on a surface _->_(—) _ <<Noun_KW>>_=_(— —)(Surface Structure)_. *): _Association_(—) the width of a groovewith aspect ratio (depth/width) 1 on the surface _->_(—) _ <<Noun_KW>>_=_(— —)(Surface Structure)_,_(Size)_. *): _Association_(—) differential equation is not valid _->_(—)  _ <<Noun_KW>>_=_(— —)(Differential Equation)_._

Verb_KW

_=_(— —)  {Break Down}_. *): _Association_(—)  surface _->_(—)  _<<Noun_KW>> _=_(— —)(Surface)_. *): _Association_(—)  differentialequation _->_(—)  _ <<Noun_KW>> _=_(— —)(Differential Equation)_.   Asthe reasoning model, I adopt the opportunistic reasoning model.

[3259] Let me begin the description of the process that is carried outin the first step of opportunistic reasoning, just below.

[3260] In the field after ‘)then(’, in the proposition ‘4)’ which is tobe finally proved, I can find the word phrase ‘surface diffusionequation’, which seemed to be mathematically well defined. This meansthat a backward reasoning, instead of a forward reasoning should becarried out in the first step of opportunistic reasoning in the presentexample.

[3261] Then, in this situation, it is recommended that the @[algorithmof making a list of ‘descriptors’ ranked in order of hit frequency]should be carried out to search ‘descriptors’ which characterize wellthe word phrase, ‘surface diffusion equation’. And then, let us assumethat two descriptors ‘_(Surface)_’ and ‘_(Differenhal Equation)_’ arelisted with high rank.

[3262] This process corresponds to a sentence in Formula. 2,

[3263]Search_usable_‘descriptors’_which_represent_suitably_the_‘consequence’_(—)

[3264] of_the_(—)

[3265] ‘hypothetical_proposition_(—)

[3266]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_(——)by_using_(—)

[3267] @[algorithm of making a list of ‘descriptors’ ranked in order ofhit frequency]_( );_.

[3268] Let us assume that we want to make an concentrated retrieval.Then, it is recommended that sentences which contains a descriptor‘_(Surface)_’ and/or ‘_(Differential Equation)_’ should be retrievedfrom the set of the keys described in “sentence pattern of ‘idealthesaurus’” stored in the ‘object-oriented knowledge base’ disclosed inthe present invention. And let us assume that no narrower ‘descriptor’of ‘_(Surface)_’ and/or ‘_(Differential Equation)_’ are found. Thesituation assumed here means that ‘_(Surface)_’ and ‘_(DifferentialEquation)_’ are the ‘descriptors’ that represent ‘surface diffusionequation’ most precisely and specifically, in the object-orientedknowledge base system used in this present example.

[3269] This process corresponds to a sentence in Formula. 2, if(someusable ‘descriptors’ are retrieved) {  if(concentrated retrieval is tobe carried out)  {   Optimize_usable_‘descriptors’_for_concentrated_(—)  “retrieval of the ‘hypothetical propositions possibly used in forward  reasoning”’_by using_‘ideal_thesaurus’( ) ;_(—)

[3270] In the present practical example, it was assumed that we werelucky enough to obtain usable ‘descriptors’.

[3271] Under the situation that we have assumed above, it is recommendedthat we should first carry out a backward reasoning. This judgment iscarried out according to if(‘descriptors’ and/or‘names-of-classification-items’ describing the ‘consequence’ of the‘hypothetical_proposition_(—)which_is_the_target_of_the_present_step_of_(—) opportunistic_reasoning’is found and/or a user of the system contrives a good‘next-best-natural-nouns’ and/or a good ‘next-best-natural-verbs’) { Carry_out_backward_reasoning_and_(—) Determine_the_hypothetical_propositions_which_is_to_be_the_(—) target_of_the_next_step_of_opportunistic_reasoning( ) ;_(—) },

[3272] in Formula. 2.

[3273] The most important verb phrase in the field below ‘)then{’ in theproposition 4} is ‘is not valid’. The meaning of this verb phrase israther dispersed. So, it is recommended that the @[algorithm of making alist of ‘names-of-classification-items’ ranked in order of hitfrequency] should be carried out for to search‘names-of-classification-items’ which characterizes well the verb phrase‘is not valid’. Then, let us assume that we make a Boolean search usingthe intersection of ‘is not valid’ and ‘_(Differential Equation)_’.Then, it is expected that ‘algorithm-of-process’ ‘_{Break Down}_’ willlisted high when, @[algorithm of making a list of‘names-of-classification-items’ ranked in order of hit frequency] iscarried out. This corresponds to the sentence in Formula. 2,

[3274] Search_usable_‘names-of-classification-items’_(—)

[3275] which_represent_suitably_the_(—)

[3276] ‘presupposition’_(—)

[3277] of_the_(—)

[3278] ‘hypothetical_proposition_(—)

[3279]which_is_the_target_of_the_present_step_of_opportunistic_reasoning’_by_using_(—)

[3280] @[algorithm of looking through ‘names-of-classification-items’ inthe order of hit frequency]_( );_.

[3281] In many cases, when a key that is just to the point wasretrieved, it is a clever way to consult all the keys, which share thesame record with the key that is just to the point if we want to make anexact reasoning.

[3282] Let us assume that we retrieve the keys described in “sentencepattern of classification” which contain the‘name-of-classification-item’, ‘_{Break Down}_’. And let us assume thatno lower class ‘name-of-classification-item’ of it is found. Thissituation assumed here means that ‘_{Break Down}_’ is a‘name-of-classification-item’ which expresses the most precisely andmost specifically the concept of “surface diffusion equation’ ‘is notvalid’”. This process corresponds to the sentence in Formula. 2,

[3283]Optimize_usable_‘names-of-classification-items’_for_concentrated_“retrievalof the ‘hypothetical propositions to be used in forwardreasoning’”_by_using_‘classification_table’_( );_(—)

[3284] Then, it is recommended that whether the ‘algorithm-of-process’‘_{Break Down}_’ is really appropriate to be used in this case or notshould be confirmed. Then, one should retrieve the keys that contains,‘_{Break Down}_’, ‘is not valid’, and ‘(Differential Equation)_’.

[3285] Then, a key described in “sentence pattern of association”,

[3286] *):

[3287] _Association_differential equation is not valid

[3288] _→_(—)

_

Noun_KW

_=_(——)(Differential Equation)_._

Verb_KW

_=_(——){Break Down}_,

[3289] should be retrieved. It is reasonable judging that this sentenceshows that the ‘_{Break Down}_’ is really suitable‘algorithm-of-process’ to be used in this situation. But this processfor conformation corresponds to the sentence in Formula. 2

[3290]confirming_the_optimized_‘names-of-classification-items’_by_using_keys_and_records_of_the_corresponding_data_if_necessary( ) ; _.

[3291] Above discussion shows that, when a concentrated retrieval is tobe carried out, then, ‘_(Surface)_’, and, ‘_(Differential Equation)_,are the ‘descriptors’ which characterizes most suitably the propositionthat “‘surface diffusion equation’ ‘is not valid’”, and, ‘_{BreakDown}_’ is the ‘name-of-classification-item’ which characterizes mostsuitably the proposition “‘surface diffusion equation’ ‘is not valid’”.

[3292] Next, let us carry out a process corresponding the function inFormula. 4,

[3293]Retrieve_‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_by_using_(—)

[3294]optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)

[3295] ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’();_.

[3296] Here, a key possibly used as a rule with which a backwardreasoning is carried out, should be retrieved. That is, we shouldretrieve here, a key described in “sentence pattern of physical and/ormathematical rules’ in whose field of below ‘)then{’, a ‘descriptor’‘_(Surface)_’ and/or ‘_(Differential Equation)_’ and/or their broader‘descriptor’ is included and/or in whose field of below ‘}then{’, an‘algorithm-of-process’ ‘{_Break Down}_’ and/or its higher class‘algorithm-of-process’ is included.

[3297] If such a retrieval is carried out, then, proposition ‘3)’ isretrieved. This proposition ‘3)’ is what is called‘Hypothetical_propositions_possibly_used_in_backward_reasoning’ inFormula. 4. If we watch the contents of ‘3)’, then, we will find that‘3)’ can be used as it is as the rule to be used in the present step ofbackward reasoning. That is, we will find that ‘3)’ is what is called“the hypothetical proposition to be used in backward reasoning” inFormula. 4.

[3298] Here, let us assume that just one appropriate proposition, i.e.‘3)’ was retrieved. In this situation we assumed here, the procedure inFormula. 4, if( too many ‘hypothetical proposition possibly used inbackward reasoning’ are retrieved, i.e. a combinatorial explosionoccurs) {  Retrieve_directly_(—) ‘Hypothetical_propositions_to_be_used_in_backward_reasoning’( ) ;_(—) if(combinatorial explosion now and/or in the future still can not beavoided)  {   Revise_(—)  optimized_‘descriptors’,_optimized_‘names-of-classification-items’,_(—)  ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’   by_(—)  Carring_out_a_process_according_to_@[algorithm of narrowing down thetarget   ‘descriptors’ and/or target‘names-of-classification-items’]_if_necessary_(—)  And_Retrieve_again,_(—)  ‘Hypothetical_propositions_possibly_used_in_backward_reasoning’_(—)  by_using_the_revised_(—)  ‘descriptors’,_‘names-of-classification-items’,_(—)  ‘next-best-natural-nouns’,_and/or,_‘next-best-natural-verbs’( ) ;_(—)  if(combinatorial explosion now and/or in the future still can not beavoided)   {    Withdraw_the_process_carried_out_according_to_the_(—)   @[algorithm of narrowing down the target ‘descriptors’ and/or target   ‘names-of-classification-items’]_(—)    and_(—)   Carry_out_a_process_according_to_(—)    @[algorithm of fusingpropositions]_to_the_‘presuppositions’_(—)   of_the_‘Hypothetical_propositions_possibly_used_in_forward_reasoning’_(—)   if_necessary( ) ;_(—)   }  }  if(neither direct retrieval nor@[algorithm of narrowing down the target  ‘descriptors’ and/or target‘names-of-classification-items’]  nor @[algorithm of fusingpropositions] works well)  {   Change_the_(—)  “decision_concerning_whether_forward_reasoning_and/or_(—)  backward_reasoning_should_be_carriedout”_(—)  made_in_previous_steps_of_opportunistic_reasoning( ) ;_(—)  } } if(too less ‘hypothetical proposition possibly used in backward reasoning’are retrieved ) {  Carry_out_@[algorithm of broadening out the target‘descriptors’ and/or target  ‘names-of-classification-items’] And_Retrieve_again,_(—) ‘Hypothetical_propositions_possibly_used_in_backward_reasoning’( );_(—)  if(@[algorithm of broadening out the target ‘descriptors’ and/ortarget  ‘names-of-classification-items’] dose not work well)  {  Change_the_(—)  “decision_concerning_whether_forward_reasoning_and/or_(—)  backward_reasoning_should_be_carriedout”_(—)  made_in_previous_steps_of_opportunistic_reasoning( ) ;_(—)  } }

[3299] may be omitted for the time being in the present step of backwardreasoning. Only when problematic situation would occur in later step ofopportunistic reasoning, then, adjustment described above should becarried out.

[3300] Next we should carry out the process which corresponding to thesentence in Formula. 4,

[3301] Determine_the_hypothetical_propositions_(—)

[3302] which_are_to_be_used_(—)

[3303] as_the_target_of_the_next_step_of_opportunistic_reasoning_(—)

[3304] on_the_basis_

[3305]of_the_‘hypothetical_propositions_to_be_used_in_backward_reasoning’_(—)

[3306] in_the_present_step_of_opportunistic_reasoning

[3307] and_(—)

[3308] of_a_hypothetical_proposition_(—)

[3309] which_is_the_target_of_the_present_step_of_backward_reasoning

[3310] of_opportunistic_reasoning_(—)

[3311] by_using_(—)

[3312] @[algorithm of sentence based object-oriented categoricalsyllogism]_and_(—)

[3313] @[algorithm of sentence based object-oriented hypotheticalsyllogism]_(—)

[3314] backward( );_.

[3315] Here, it should be noted that,

[3316] if

[3317] the hypothetical proposition,

[3318] 5):

[3319] _RULE_(—)

[3320] a lemma

[3321] _states_if(

[3322] _S=_ the width of a groove with aspect ratio (depth/width) 1 onthe surface of metal

[3323] aluminum at 500° C. _V=_is _C=_(—)32 Å;_(—)

[3324] )then{

[3325] _S=_ surface diffusion coefficient_

Noun_KW

_=_(— —)(Surface)_,_(Diffusion Coefficient)_.

[3326] _V=_ is not valid near the groove with aspect ratio (depth/width)1 on the surface of metal aluminum at 500° C.

Verb_KW

_=_(— —){Break Down}_.;_(—)

[3327] };_(—)

[3328] is proved to be true,

[3329] then,

[3330] hypothetical proposition ‘4)’ can be proved to be true, byinserting the hypothetical proposition ‘3)’ into ‘5)’ on the basis ofusual syllogism (i.e. “if ‘a→b’ and ‘b→c’, then ‘a→c’”) by using abackward reasoning, in a backward reasoning in the present step ofopportunistic reasoning.

[3331] Proposition ‘4)’ is‘the_Proposition_to_be_finally_proven_by_using_the_system’ in Formula.2, but in the present first step of opportunistic reasoning, Proposition‘4)’ is regarded as the ‘hypothetical proposition that is the target ofthe present step of opportunistic reasoning’.

[3332] And the hypothetical proposition ‘3)’ is a “hypotheticalproposition to be used in backward reasoning” in the first step ofopportunistic reasoning (See Formula. 4). And the hypotheticalproposition ‘5)’ is the “hypothetical proposition which is to be used asthe target of the next step of opportunistic reasoning” (See Formula.4).

[3333] Similar steps of opportunistic reasoning, in which backwardreasoning is carried out in the case of the present example, will beshown just below.

[3334] In the second step of opportunistic reasoning, in a similar way,it should be noted that, the hypothetical proposition ‘5)’ should beused as the hypothetical proposition which is the target of the secondstep of opportunistic reasoning.

[3335] In the second step, let us assume that proposition ‘2)’ andproposition ‘7)’, which are readily used to make a backward reasoning,are easily retrieved. Thus, let us assume that in the second step ofopportunistic reasoning of the present example, a backward reasoning,not a forward reasoning, is carried out, because of the similar reasonwith which I judged to use a backward reasoning in the first step ofopportunistic reasoning. Then, it should be noted that,

[3336] if

[3337] a hypothetical proposition,

[3338] 6):

[3339] _RULE_(—)

[3340] a lemma

[3341] _states_if(

[3342] _S=_the width of a groove with aspect ratio (depth/width) 1 onthe surface of metal aluminum at 500° C._

Noun_KW

_=_(— —)(Aluminum)_,_(Metal Surface)_,_(Surface Structure)_._V=_is_C=_(—)32 Å;_)then{

[3343] _S=_ the typical size of a surface structure_

Noun_KW

=_(— —)(Surface Structure)_,_(Size)_._V=_is _C=_ less than the mean freepath (of the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C.)_ z,902 Noun_KW

_=_(— —)(Aluminum)_,_(Metal Surface)_,_(AdsorbedAtom)_,_(Migration)_,_(Mean Free Path)_. ;_} ;_,

[3344] is proved to be true, under the previously described premise,then, proposition ‘5)’ is proved to be true by inserting ‘6)’ and ‘7)’,into proposition ‘2)’ on the basis of @[algorithm of sentence basedobject-oriented hypothetical syllogism]. and usual syllogism (i.e. “if‘a→b’ and ‘b→c’, then ‘a→c’”)

[3345] The categorical proposition, ‘7)’ is used as the minor premise of@[algorithm of sentence based object-oriented categorical syllogism],which leads

[3346] the categorical proposition,

[3347] “_S=_ the width of a groove with aspect ratio (depth/width) 1 onthe surface of metal aluminum at 500° C. _V=_is _C=_(—)32 Å;_”, (i.e.consequence of ‘5)’),

[3348] from a categorical proposition,

[3349] _S=_ the typical size of a surface structure_

Noun_KW

_=_(— —)(Surface Structure)_,_(Size)_._V=_is _C=_ less than the meanfree path (of the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C.)_

Noun_KW

_=_(— —)(Aluminum)_,_(Metal Surface)_,_(AdsorbedAtom)_,_(Migration)_,_(Mean Free Path)_. ;_} ;_. (i.e. conclusion of‘6)’).

[3350] The hypothetical propositions ‘2)’ is the “hypotheticalproposition to be used in backward reasoning” (see Formula. 4). And thehypothetical proposition ‘6)’ is the “hypothetical proposition which isto be used as the target of the next step of opportunistic reasoning”(see Formula. 4).

[3351] Therefore, proposition ‘5)’ can be proved to be true ifproposition ‘6)’ is proved to be true. In the next step, I must try toprove ‘6)’.

[3352] In the third step of opportunistic reasoning, in a similar way,it should be noted that, the hypothetical proposition ‘6)’ should beused as the hypothetical proposition which is the target of the thirdstep of opportunistic reasoning. Here, it should be noted that,

[3353] if a hypothetical proposition,

[3354] 9):

[3355] _RULE_(—)

[3356] a lemma

[3357] _states_if( _S=_ the typical size of a surface structure_

Noun_KW

_=_(— —)(Surface Structure)_,_(Size)_._V=_is _C=_(—)32 Å;_)then{_S=_thetypical size of a surface structure_

Noun_KW

_=_(— —)(Surface Structure)_,_(Size)_._V=_is _C=_ less than the meanfree path (of the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C.)_

Noun_KW

_=_(— —)(Aluminum)_,_(Metal Surface)_,_(AdsorbedAtom)_,_(Migration)_,_(Mean Free Path)_. ;_ };_(—)

[3358] is proved to be true, then, proposition ‘6)’ can be proved to betrue by inserting ‘8)’ into ‘9)’ on the basis of usual syllogism (i.e.“if ‘a→b’ and ‘b→c’, then ‘a→c’”).

[3359] Therefore, proposition 6) can be proved to be true if proposition9) is proved to be true. In the following discussion, I must try toprove 9).

[3360] In the fourth step of opportunistic reasoning, in a similar way,backward reasoning is carried out. It should be noted here that, thehypothetical proposition ‘9)’ should be used as the hypotheticalproposition which is the target of the fourth step of opportunisticreasoning. Here, it should be noted that,

[3361] if

[3362] a proposition,

[3363] 11):

[3364] _RULE_(—)

[3365] a lemma

[3366] _states_if(

[3367] _S=_the typical size of a surface structure_

Noun_KW

_=_(— —)(Surface Structure)_,_(Size)_._V=_is _C=_ less than 60 Å;_)then{

[3368] _S=_ the typical size of a surface structure_

Noun_KW

_=_(— —)(Surface Structure)_,_(Size)_._V=_is _C=_(‘) less than the meanfree path (of the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C.)_

Noun_KW

_=_(— —)(Aluminum)_,_(Metal Surface)_,_(AdsorbedAtom)_,_(Migration)_,_(Mean Free Path)_. ;_ };_(—)

[3369] is proved to be true,

[3370] then,

[3371] proposition ‘9)’ can be proved to be true by inserting thehypothetical proposition ‘10)’ into proposition ‘11)’ on the basis ofusual syllogism (i.e. “if ‘a→b’ and ‘b→c’, then ‘a→c’”).

[3372] The hypothetical proposition ‘10)’ is the “hypotheticalproposition to be used in backward reasoning” (see Formula. 4). And thehypothetical proposition ‘11)’ is the “hypothetical proposition which isto be used as the target of the next step of opportunistic reasoning” inFormula. 4.

[3373] Therefore, proposition ‘9)’ can be proved to be true ifproposition ‘11)’ is proved to be true. In the following discussion, Imust try to prove ‘11)’.

[3374] In the fifth step of an opportunistic reasoning, in a similarway, it should be noted that, the hypothetical proposition ‘11)’ shouldbe used as the hypothetical proposition which is the target of the fifthstep of opportunistic reasoning.

[3375] If proposition ‘1)’ is inserted into proposition ‘11)’,

[3376] then,

[3377] a proposition ‘12)’, as,

[3378] 12):

[3379] _RULE_(—)

[3380] a trivial rule

[3381] _states_if(

[3382] _S=_the typical size of a surface structure_

Noun_KW

_=_(— —)(Surface Structure)_, _(Size)_. _V=_is_C=_(— less than the mean free path (of the migration of Al atoms adsorbed)

[3383] on the surface of metal aluminum at 500° C.)_

Noun_KW

_=_(— —)

[3384] (Aluminum)_,_(Metal Surface)_,_(AdsorbedAtom)_,_(Migration)_,_(Mean Free Path)_;_)then{

[3385] _S=_ the typical size of a surface structure_

Noun_KW

_=_(— —)(Surface Structure)_,_(Size)_. _V=_is _C=_ less than the meanfree path (of the migration of Al atoms adsorbed on the surface of metalaluminum at 500° C.)_

Noun KW

_=_(— —)(Aluminum)_,_(Metal Surface)_,_(AdsorbedAtom)_,_(Migration)_,_(Mean Free Path)_;_};_(—)

[3386] is obtained.

[3387] This hypothetical proposition, ‘12)’ is trivial. Thus hire, Ijudge that the hypothetical proposition, ‘4)’ has proven to be truth.

[3388] This corresponds to the sentence, if( Problem is solved ) {break;_(—) },

[3389] in Formula. 2.

[3390] As reasoning shown above has not lost strictness, the process ofFormula. 2, /* Verification of the Result of above OpportunisticReasoning */ if( the strictness of the reasoning of a step becomes lowafter for example carrying_out_the_@[algorithm of fusing propositions] ){Re-prove_all_the_steps_of_the_proof_by_a_series_of_steps_of_more_exact_(—)opportunistic_reasoning( ) ;_(—) }

[3391] can be omitted.

[3392] Above example shows that if one wants to embody a practicalreasoning by using an object-oriented knowledge base system disclosed inthe present invention, then, one must a very large set of seeminglytrivial propositions must be piled up in the object-oriented knowledgebase system. These seemingly trivial propositions may be dealt with byhuman hand, but it is preferred that a user of the object-orientedknowledge base system should record all the propositions which they usedduring their a reasoning. It is recommended that a knowledge engineershould judge such propositions whether to be of universal use and/or tobe only of special use. If and when such a proposition is judged to beof universal use by a knowledge engineer, then it is recommended thatthe proposition should be recorded as a rule and/or as a fact in theobject-oriented knowledge base.

[3393] Especially when an object-oriented knowledge base systemdisclosed in the present invention is used to embody a large scaleknowledge management system, it is essentially necessary to record notonly clearly nontrivial propositions but also seemingly trivialpropositions as data of the retrieval system. It is recommended that anaxiom system should be constructed by a knowledge engineer for theobject-oriented knowledge base system when the system of knowledge isvery frequently used and/or the system is knowledge must have strictexactness.

[3394] Although the description above contains many specifications,these should not be construed as limiting the scope of the invention butmerely providing illustrations of some of the presently profferedembodiments of this invention.

4. Some Comment

[3395] Mr. Nanao Nakamura's opinion (Private communication with me) issummarized that,

[3396] 1) Invaluable information can be obtained by reading a book ofthesaurus, which makes a man clever. ({circle over (∘)}“NanaoNakamura”).

[3397] 2) A category is strict and solid existence, and a concept is avague idea. ({circle over (∘)}“Nanao Nakamura”).

[3398] 3) The history of progress made by means of the humanintelligence surely began with dichotomy: division of the heaven and theearth, which is described in many myths in Japan is an example. ({circleover (∘)}“Nanao Nakamura”),

[3399] which are not publicly known.

[3400] However, any of these three sentences is not publicly acceptedand/or known. In addition, no perfect and concrete relation amongthesauruses, categories, concepts and dichotomy has been revealed and/ordescribed systematically by anyone thus far. In a word, these threesentences are exactly a poem, but are not sentences describing skill,technology, and/or science. And these three sentences have not beenconsciously or explicitly understood by anyone in a scientific wayand/or in a technical manner, thus far.

[3401] The system disclosed in the present invention is a scientificsystem based on a close relation among thesauruses, categories, conceptsand dichotomy, and the system disclosed in the present inventionprovides a scientific technology embodying an original object-orientedknowledge base system.

5. Conclusion, Ramification, and Scope

[3402] Thus, the reader will see that the knowledge base systemdisclosed in the present invention provides

[3403] a way of inference using

[3404] not only rules and/or facts that are described in a mathematicalformula, in which exact and detailed mathematical definitions of wordsare given,

[3405] but also rules and/or facts that are described in a sentencelinguistically, in which only a lexical definition of words are given.

[3406] In an ideal dictionary, lexical meanings of words, which aregiven in an object-oriented style and are stored, and a relation betweenwords is described in detail using a sentence based style,

[3407] i.e. what a word seem like from other words is described indetail, but, unlike in the mathematical definition,

[3408] the detailed procedure how the words interacts with other wordsis not necessarily strictly defined.

[3409] In a word, a word that has a lexical meaning but dose not havemathematical definition, i.e. a ‘black box’, may be used in a sentenceused as a rule and/or as a fact in the present object-oriented knowledgebase system.

[3410] In the present invention, I disclosed a way of inference, which Icall @[algorithm of sentence based object-oriented categoricalsyllogism], with which facts and/or rules described using such ‘blackboxes’ can be used as a rule and/or as a fact. In an object-orientedknowledge base system disclosed in the present invention, @[algorithm ofsentence based object-oriented categorical syllogism] is made full useof,

[3411] by making use of

[3412] the hierarchy of nouns stored in an ‘ideal thesaurus’

[3413] and

[3414] the hierarchy of verbs stored in a ‘classification table’.

[3415] An ‘ideal thesaurus’ is designed on the basis of theobject-oriented style. And, a ‘classification table’ is constructedusing the idea of ‘dichotomy’ and using the method with which lexicaldefinition of verbs are given by means of quasi-C codes.

[3416] As a result, in the knowledge base system disclosed in thepresent invention, exact and/or exhaustive inference can be carried outnot only on mathematical equations,

[3417] but also on

[3418] sentences written in English,

[3419] only if the lexical meanings of the words are given according tothe way disclosed in the present invention and the sentence is writtenin a style disclosed in the present invention.

[3420] Therefore, not only axiom systems in Physics and/or inMathematics, but also system of knowledge of intelligent experts,including metallurgists and/or computer programmers, can be embodieddirectly by using the present knowledge base system.

[3421] In a word, the present knowledge base system is suitable to beused to embody an expert system.

[3422] An object-oriented knowledge base system disclosed in the presentinvention can be regarded as an automatic theorem proving system in itsbroader meaning, and can be used as a CAD for coding of computerprogram, an intelligent on-line manual of a machine, and a retrievalsystem of knowledge management system and of a digital nervous system.

[3423] An object-oriented knowledge base system disclosed in the presentsystem may exist in a bulk on a machine, but may exist as a distributedsystem of computers connected via the Internet and/or via a LAN forexample.

[3424] The information of computer code of ‘inference mechanism’, and/orcontents of the ‘object-oriented knowledge base’, and/or contents of an‘object-oriented knowledge base management system’, etc. may be storedin a bulk for example in a ‘means for storing knowledge base system’such as a storing media, in a memory, and/or in an application specificintegrated circuit.

[3425] But the whole and/or a part of the information of computer codeof ‘inference mechanism’, and/or of contents of the ‘object-orientedknowledge base’ and/or contents of an ‘object-oriented knowledge basemanagement system’, etc. may by stored in separate ‘means for storingknowledge base system’, such as in more than two storing media, in morethan two memories, and/or in more than two application specificcircuits.

[3426] Accordingly, the scope of the invention should be determined notby the embodiments shown, but the appended claims and their legalequivalents.

(References)

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[3456]

BRIEF DESCRIPTION OF THE DRAWINGS

[3457]FIG. 1 shows a recommended constitution of an object-orientedknowledge base system whose body of information is stored on a ‘meansfor storing knowledge base system’.

[3458]FIG. 2 shows a recommended constitution of an ideal thesaurus.

[3459]FIG. 3 shows a recommended constitution of an ideal classificationtable.

[3460]FIG. 4 shows a recommended constitution of a ‘means for givingdefinition of higher class algorithm-of-process and lower classalgorithm-of-process’.

[3461]FIG. 5 shows a recommended constitution of rules.

[3462]FIG. 6 shows a recommended constitution of an ‘object-orientedknowledge base management system’.

[3463]FIG. 7 shows some examples what are used as the name of thefunction in ‘means for describing a function used as a rule’.

[3464]FIG. 8 shows a recommended constitution of means for storing dataproviding the ability of association.

[3465]FIG. 9 shows a recommended style of reasoning as a ‘means forcarrying out an inference’.

[3466]FIG. 10 shows the aim of each step of opportunistic reasoning.

[3467]FIG. 11 shows a recommended constitution of each step ofopportunistic reasoning.

[3468]FIG. 12 shows a recommended constitution of therules-for-reasoning used in a step of an opportunistic reasoning.

[3469]FIG. 13 shows a recommended constitution of “means for getting‘descriptors’ that are used to make a query to get the “candidates ofthe ‘rules-for-reasoning”.

[3470]FIG. 14 shows a recommended constitution of the “means for getting‘names-of-classification-items’ that are used to a make query to get the“candidates of the ‘rules-for-reasoning”.

[3471]FIG. 15 shows a recommended constitution of the procedure of‘retrieval of the candidates of the rules-for-reasoning’.

[3472]FIG. 16 shows a recommended constitution of the ‘means for pickingup only the rules-for-reasoning from the candidates of therules-for-reasoning got in the retrieval of FIG. 15’.

[3473]FIG. 17 shows a recommended constitution of ‘means for determiningthe hypothetical propositions which are to be used as the target of thenext step of opportunistic reasoning’.

[3474]FIG. 18 shows a recommended constitution of the “means forretrieving directly the ‘rules-for-reasoning”.

[3475]FIG. 19 shows a ‘digital computing system’ with a ‘means forstoring knowledge base system’.

[3476]FIG. 20 shows a ‘digital computing system’ and a ‘means forstoring data’.

[3477]FIG. 21 shows some examples of ‘means for storing knowledge basesystem’.

[3478]FIG. 22 shows examples of a ‘digital computing systems’ in itsstrict meaning. But in the present invention, I regard a ‘digitalcomputing system’ as a kind of a computer.

[3479]FIG. 23 shows a classification table of some intransitive verbsand some transitive verbs.

1. Means for storing knowledge base system in which a set selected fromthe group consisting of the universal set and the subsets, of aplurality of data which constitute an object-oriented knowledge basesystem is stored, in said object-oriented knowledge base systemcomprising: (a) an object-oriented knowledge base whose body ofinformation includes data selected from the group consisting of (a-1) anideal thesaurus in which hierarchy of nouns is stored, comprising(a-1-1) a plurality of keys described using means for storing data ofideal thesaurus, in which a noun with broader meaning is described as abroader descriptor, and a noun with narrower meaning is described as anarrower descriptor, (a-2) an ideal classification table in whichhierarchy of verbs is recorded, comprising (a-2-1) a plurality of keysdescribed using means for storing data of classification table, in whicha verb with broader meaning is described as a higher classname-of-classification-item and a verb with narrower meaning isdescribed as a lower class name-of-classification-item, and, (a-3) aplurality of rules described by using (a-3-1) a plurality of keysdescribed using means for storing data used as rules, (b) anobject-oriented knowledge base management system whose body ofinformation includes data selected from the group consisting of (b-1) anideal dictionary wherein lexical definition of verbs and nouns are givenby using keys selected from the group consisting of (b-1-1) a pluralityof keys described using means for storing data that define objects, forgiving the lexical definition of an ideal noun, (b-1-2) a plurality ofkeys described using means for implementation ofalgorithms-of-processes, for giving the lexical definition of an idealverb, and, (b-1-3) a plurality of keys described using means fordescribing the function of a verb, for giving the lexical definition ofan ideal verb, (b-2) means for giving definition of broader descriptorand narrower descriptor, coupled to said object-oriented knowledge base,for constructing the hierarchy of nouns in said ideal thesaurus whereinas the basis on which said definition of broader descriptor and narrowerdescriptor is given, (b-2-1) the plurality of keys described using meansfor storing data that define objects, are used, and, (b-3) means forgiving definition of higher class algorithm-of-process and lower classalgorithm-of-process, coupled to said object-oriented knowledge base,for constructing the hierarchy of verbs in said ideal classificationtable wherein as the basis on which said definition of higher classalgorithm-of-process and lower class algorithm-of-process is given,(b-3-1) the plurality of keys described using means for implementationof algorithms-of-processes, are used, and, (c) means for carrying out ainference, coupled to said object-oriented knowledge base wherein (c-1)means for carrying out sentence based object-oriented categoricalsyllogism is used wherein as the basis on which said sentence basedobject-oriented categorical syllogism is carried out, (c-1-1) said idealthesaurus, and (c-1-2) said ideal classification table, are used,whereby, said object-oriented knowledge base system processes not onlyrules and questions written as mathematically well defined equations butalso rules and questions written as English sentences written insentence pattern of one of five basic sentence patterns of Englishgrammar.
 2. The means for storing knowledge base system of claim 1wherein said ideal dictionary further uses (a) a plurality of keysdescribed using means for storing the list of lexical meanings of anatural word.
 3. The means for storing knowledge base system of claim 1wherein said keys described using means for storing data used as rulescomprises plurality of keys described according to (a) means fordescribing sentences according to a simple English grammar.
 4. The meansfor storing knowledge base system of claim 1 wherein said means forgiving definition of higher class algorithm-of-process and lower classalgorithm-of-process includes (a) means for making more specific meaningof a verb from that of a verb whose meaning is more general.
 5. Themeans for storing knowledge base system of claim 1 wherein further (a)means for fusing propositions, which prevents a combinatorial explosion,is used during carrying out said means for carrying out a inference. 6.The means for storing knowledge base system of claim 1 wherein (a) meansfor storing data that strictly define objects, is used as said means forstoring data that define objects.
 7. The means for storing knowledgebase system of claim 1 wherein (a) means for storing data of idealthesaurus in a formal way is used as said means for storing data ofideal thesaurus.
 8. The means for storing knowledge base system of claim1 wherein (a) means for storing data of classification table in a formalway is used as said means for storing data of classification table. 9.The means for storing knowledge base system of claim 1 wherein (a) meansfor storing data used as rules in a formal way is used as said means forstoring data used as rules
 10. The means for storing knowledge basesystem of claim 1 wherein (a) means for storing data about instances ofsolving problems, is used as said means for storing data used as rules.11. The means for storing knowledge base system of claim 1 wherein (a)means for describing a function used as a rule is used as said means forstoring data used as rules.
 12. The means for storing knowledge basesystem of claim 11 wherein as the name of the function of claim 11, aname selected from the group consisting of (a) the name of a subroutine,(b) the name of a computer program, and, (c) the name of an unitoperation described in a manual is used.
 13. The means for storingknowledge base system of claim 1 wherein the body of information of saidobject-oriented knowledge base further includes (a) data comprising(a-1) a plurality of keys described using means for storing dataproviding the ability of association.
 14. The means for storingknowledge base system of claim 13 wherein (a) means for storing dataproviding the ability of association in a strict way is used as saidmeans for storing data providing the ability of association.
 15. Themeans for storing knowledge base system of claim 1 in which a setselected from the group consisting of the universal set and the subsets,of a plurality of data which constitute the object-oriented knowledgebase system is stored, in said object-oriented knowledge base systemcomprising: (a) the object-oriented knowledge base whose body ofinformation includes data selected from the group consisting of (a-1)the ideal thesaurus in which hierarchy of nouns is stored, comprising(a-1-1) the plurality of keys described using means for storing data ofideal thesaurus, in which a noun with broader meaning is described as abroader descriptor, and a noun with narrower meaning is described as anarrower descriptor, (a-2) the ideal classification table in whichhierarchy of verbs is recorded, comprising (a-2-1) the plurality of keysdescribed using means for storing data of classification table, in whicha verb with broader meaning is described as a higher classname-of-classification-item and a verb with narrower meaning isdescribed as a lower class name-of-classification-item, (a-3) aplurality of rules described by using (a-3-1) the plurality of keysdescribed using the means for storing data providing the ability ofassociation, and, (a-4) the plurality of rules described by using keysselected from the group consisting of (a-4-1) the plurality of keysdescribed using means for storing data used as rules wherein the(a-4-1-1) means for describing sentences according to a simple Englishgrammar. is used, and, (a-4-2) the plurality of keys described using themeans for storing data about instances of solving problems, (b) anobject-oriented knowledge base management system whose body ofinformation includes data selected from the group consisting of (b-1)the ideal dictionary wherein lexical definition of verbs and nouns aregiven by using keys selected from the group consisting of (b-1-1) theplurality of keys described using means for storing the list of lexicalmeanings of a natural word, (b-1-2) the plurality of keys describedusing means for storing data that define objects, for giving the lexicaldefinition of an ideal noun, (b-1-3) the plurality of keys describedusing means for implementation of algorithms-of-processes, for givingthe lexical definition of an ideal verb, and, (b-1-4) the plurality ofkeys described using means for describing the function of a verb isused, for giving the lexical definition of an ideal verb, (b-2) themeans for giving definition of broader descriptor and narrowerdescriptor, coupled to said object-oriented knowledge base, forconstructing the hierarchy of nouns in said ideal thesaurus wherein asthe basis on which said definition of broader descriptor and narrowerdescriptor is given, (b-2-1) the plurality of keys described using meansfor storing data that define objects, are used, and, (b-3) the means forgiving definition of higher class algorithm-of-process and lower classalgorithm-of-process, coupled to said object-oriented knowledge base,for constructing the hierarchy of verbs in said ideal classificationtable wherein as the basis on which said definition of higher classalgorithm-of-process and lower class algorithm-of-process is given,(b-3-1) the plurality of keys described using means for implementationof algorithms-of-processes, are used, and, (c) the means for carryingout a inference, coupled to said object-oriented knowledge base whereinas the style of the reasoning, (c-1) an opportunistic reasoning isadopted wherein reasoning selected from the group consisting of (c-1-1)forward reasoning and (c-1-2) backward reasoning is carried out in eachstep of said opportunistic reasoning, and, the aim of each step of saidopportunistic reasoning is to try to logically prove (c-1-3) ahypothetical proposition that is the target of the present step ofopportunistic reasoning, said hypothetical proposition which is thetarget of the present step of opportunistic reasoning having apresupposition and a consequence, and, (c-2) each step of saidopportunistic reasoning comprises set of means selected from the groupconsisting of, (c-2-1) means for getting rules-for-reasoning, (c-2-1-0)said rules-for-reasoning equals to (c-2-1-0-1) hypothetical propositionsto be used in forward reasoning in the case of forward reasoning, saidhypothetical propositions to be used in forward reasoning having apresupposition and a consequence, and equal to (c-2-1-0-2) hypotheticalpropositions to be used in backward reasoning in the case of backwardreasoning, said hypothetical propositions to be used in backwardreasoning having a presupposition and a consequence wherein algorithmsselected from the group consisting of, (c-2-1-1) means for gettingdescriptors that are used to make a query to get the candidates of therules-for-reasoning comprising (c-2-1-1-1) means for making a list ofdescriptors ranked in order of hit frequency wherein (c-2-1-1-1-1) thedescriptors in said list are ones associated with (c-2-1-1-1-1-1)anatural word which characterizes the presupposition of said hypotheticalproposition that is the target of the present step of opportunisticreasoning in the case of forward reasoning, and (c-2-1-1-1-1-2)a naturalword which characterizes the consequence of said hypotheticalproposition that is the target of the present step of opportunisticreasoning in the case of backward reasoning, and, (c-2-1-1-1-2) as thebasis from which the descriptors which are to be used to make(c-2-1-1-1-2-1) a query to get the candidates of therules-for-reasoning, are fetched, the data selected from the groupconsisting of (c-2-1-1-1-2-2) the plurality of keys described usingmeans for storing data providing the ability of association,(c-2-1-1-1-2-3) the plurality of keys described using means for storingthe list of lexical meanings of a natural word, and, (c-2-1-1-1-2-4)other plurality of keys in said object-oriented knowledge base, areused, (c-2-1-2) means for getting the names-of-classification-items thatare used to make said query to get the candidates of therules-for-reasoning comprising (c-2-1-2-1) means for making a list ofnames-of-classification-items ranked in order of hit frequency, wherein(c-2-1-2-1-1) the names-of-classification-items in said list are onesassociated with (c-2-1-2-1-1-1)a natural word which characterizes thepresupposition of said hypothetical proposition that is the target ofthe present step of opportunistic reasoning in the case of forwardreasoning, and, (c-2-1-2-1-1-2) a natural word that characterizes theconsequence of said hypothetical proposition which is the target of thepresent step of reasoning in the case of backward reasoning, and,(c-2-1-2-1-2) as the basis from which the names-of-classification-itemsthat are to be used to make (c-2-1-2-1-2-1) said query to get thecandidates of the rules-for-reasoning, are fetched, the data selectedfrom the group consisting of (c-2-1-2-1-2-2) the plurality of keysdescribed using means for storing data providing the ability ofassociation, (c-2-1-2-1-2-3) the plurality of keys described using meansfor storing the list of lexical meanings of a natural word, and,(c-2-1-2-1-2-4) other plurality of keys in said object-orientedknowledge base, are used, (c-2-1-3) a retrieval of the candidates of therules-for-reasoning using said query selected from the group consistingof the query of (c-2-1-1) and the query of (c-2-1-2) wherein saidcandidates of the rules-for-reasoning is a hypothetical propositionwhose presupposition is hit upon during a Boolean search using thedescriptors of (c-2-1-1) and the names-of-classification-items of(c-2-1-2) in the in the case of forward reasoning and whose consequenceis hit upon during a Boolean search using the descriptors of (c-2-1-1)and the names-of-classification-items of (c-2-1-2) in the case ofbackward reasoning, and said candidates of the rules-for-reasoning areretrieved from the basis comprising the data selected from the groupconsisting of (c-2-1-3-1) the plurality of keys described using meansfor storing data used as rules, (c-2-1-3-2) the plurality of keysdescribed using the means for storing data about instances of solvingproblems, and, (c-2-1-3-3) the plurality of keys described using meansfor describing a function used as a rule, are used, (c-2-1-4) means forpicking up only the rules-for-reasoning from the candidates of therules-for-reasoning got in the retrieval of (c-2-1-3) wherein out of thecandidates of the rules-for-reasoning, only (c-2-1-4-1) the rules whosepresupposition is derived from the presupposition of the hypotheticalproposition that is the target of the present step of opportunisticreasoning, by using the (c-2-1-4-1-1) means for carrying out sentencebased object-oriented hypothetical syllogism are picked out as therules-for-reasoning, in the case of forward reasoning, and, only(c-2-1-4-2) the rules from whose consequence, the consequence of thehypothetical proposition that is the target of the present step ofopportunistic reasoning is derived by using the (c-2-1-4-2-1) means forcarrying out sentence based object-oriented hypothetical syllogism, arepicked out as the rules-for-reasoning, in the case of backwardreasoning, and, said means for picking up only the rules-for-reasoningfrom the candidates of the rules-for-reasoning got in the retrieval of(c-2-1-3) is defined by using the (c-2-1-4-3) means for carrying outsentence based object-oriented hypothetical syllogism the procedure ofwhich is defined on the basis of the (c-2-1-4-3-1) means for carryingout sentence based object-oriented categorical syllogism wherein as thebasis on which the sentence based object-oriented categorical syllogismis carried out, (c-2-1-4-3-1-1) said ideal thesaurus, (c-2-1-4-3-1-2)said ideal classification table, and, (c-2-1-4-3-1-3) said hypotheticalproposition which is the target of the present step of opportunisticreasoning, are used, and, (c-2-1-5) means for retrieving directly therules-for-reasoning wherein as the basis from which therules-for-reasoning are retrieved, data selected from the groupconsisting of (c-2-1-5-1) the plurality of keys described using meansfor storing data used as rules, (c-2-1-5-2) the plurality of keysdescribed using the means for storing data about instances of solvingproblems, and, (c-2-1-5-3) the plurality of keys described using meansfor describing a function used as a rule are used, to retrieverules-for-reasoning, whose presupposition is derived by using the(c-2-1-5-4) means for carrying out sentence based object-orientedhypothetical syllogism from the presupposition of the (c-2-1-5-5)hypothetical proposition which is the target of the present step ofopportunistic reasoning, in the case of forward reasoning, and, toretrieve rules-for-reasoning, from whose consequence, the consequence ofthe (c-2-1-5-6) hypothetical proposition which is the target of thepresent step of opportunistic reasoning is derived by using the(c-2-1-5-7) means for carrying out sentence based object-orientedhypothetical syllogism, in the case of backward reasoning, wherein theprocedure for the means for carrying out sentence based object-orientedhypothetical syllogism is defined on the basis of the (c-2-1-5-8) meansfor carrying out sentence based object-oriented categorical syllogism,wherein as the basis on which the sentence based object-orientedcategorical syllogism is carried out, (c-2-1-5-8-1) said idealthesaurus, (c-2-1-5-8-2) said ideal classification table, and,(c-2-1-5-8-3) said hypothetical proposition which is the target of thepresent step of opportunistic reasoning, are used, (c-2-2) means foravoiding combinatorial explosion when too many number of saidrules-for-reasoning are retrieved wherein means selected from the groupconsisting of, (c-2-2-1) means for narrowing down the targetdescriptors, (c-2-2-2) means for narrowing down the targetnames-of-classification-items and (c-2-2-3) means for fusingpropositions, are used, and then, the rules-for-reasoning are retrievedagain, (c-2-3) means for making more exhaustive retrieval when too lessnumber of said rules-for-reasoning are retrieved wherein means selectedfrom the group consisting of, (c-2-3-1) means for broadening out thetarget descriptors and (c-2-3-2) means for broadening out the targetnames-of-classification-items, are used, and then, therules-for-reasoning are retrieved again, and, (c-2-4) means fordetermining the hypothetical propositions that are to be used as thetarget of the next step of opportunistic reasoning, as the basis fromwhich said hypothetical propositions which are to be used as the targetof the next step of opportunistic reasoning are retrieved, data selectedfrom the group consisting of (c-2-4-1) said rules-for-reasoning, and,(c-2-4-2) said hypothetical proposition which is the target of thepresent step of opportunistic reasoning, are used, whereby, saidobject-oriented knowledge base system processes not only rules andquestions written as mathematically well defined equations but alsorules and questions written as English sentences written in sentencepattern of one of five basic sentence patterns of English grammar, andgives answers not only written as mathematically well defined equationsbut also written as English sentences written in sentence pattern of oneof five basic sentence patterns of English grammar.
 16. Anobject-oriented knowledge base system implemented in a digital computercomprising (a) a digital computing system; (b) A means for storing dataused for said digital computer system in which a set selected from thegroup consisting of the universal set and the subsets, of a plurality ofdata which constitute an object-oriented knowledge base system isstored, and, (c) an input device, used for said digital computer system,in said object-oriented knowledge base system comprising: (d) anobject-oriented knowledge base whose body of information includes dataselected from the group consisting of (d-1) an ideal thesaurus in whichhierarchy of nouns is stored, comprising (d-1-1) a plurality of keysdescribed using means for storing data of ideal thesaurus, in which anoun with broader meaning is described as a broader descriptor, and anoun with narrower meaning is described as a narrower descriptor, (d-2)an ideal classification table in which hierarchy of verbs is recorded,comprising (d-2-1) a plurality of keys described using means for storingdata of classification table, in which a verb with broader meaning isdescribed as a higher class name-of-classification-item and a verb withnarrower meaning is described as a lower classname-of-classification-item, and, (d-3) a popularity of rules describedby using keys comprising (d-3-1) a plurality of keys described usingmeans for storing data used as rules, (e) an object-oriented knowledgebase management system whose body of information includes data selectedfrom the group consisting of (e-1) an ideal dictionary wherein lexicaldefinition of verbs and nouns are given by using keys selected from thegroup consisting of (e-1-1) a plurality of keys described using meansfor storing data that define objects, for giving the lexical definitionof an ideal noun, (e-1-2) a plurality of keys described using means forimplementation of algorithms-of-processes, for giving the lexicaldefinition of an ideal verb, and, (e-1-3) a plurality of keys describedusing means for describing the function of a verb, for giving thelexical definition of an ideal verb, (e-2) means for giving definitionof broader descriptor and narrower descriptor, coupled to saidobject-oriented knowledge base, for constructing the hierarchy of nounsin said ideal thesaurus wherein as the basis on which said definition ofbroader descriptor and narrower descriptor is given, (e-2-1) theplurality of keys described using means for storing data that defineobjects, are used, and, (e-3) means for giving definition of higherclass algorithm-of-process and lower class algorithm-of-process, coupledto said object-oriented knowledge base, for constructing the hierarchyof verbs in said ideal classification table wherein as the basis onwhich said definition of higher class algorithm-of-process and lowerclass algorithm-of-process is given, (e-3-1) the plurality of keysdescribed using means for implementation of algorithms-of-processes, areused, and, (f) means for carrying out a inference, coupled to saidobject-oriented knowledge base wherein (f-1) means for carrying outsentence based object-oriented categorical syllogism is used wherein asthe basis on which the sentence based object-oriented categoricalsyllogism is carried out, (f-1-1) said ideal thesaurus, and (f-1-2) saidideal classification table, are used, whereby, said object-orientedknowledge base system processes not only rules and questions written asmathematically well defined equations but also rules and questionswritten as English sentences written in sentence pattern of one of fivebasic sentence patterns of English grammar.
 17. The object-orientedknowledge base system implemented in a digital computer of claim 16wherein said ideal dictionary further uses (a) a plurality of keysdescribed using means for storing the list of lexical meanings of anatural word.
 18. The object-oriented knowledge base system implementedin a digital computer of claim 16 wherein said keys described usingmeans for storing data used as rules comprises a plurality of keysdescribed according to (a) means for describing sentences according to asimple English grammar.
 19. The object-oriented knowledge base systemimplemented in a digital computer of claim 16 wherein said means forgiving definition of higher class algorithm-of-process and lower classalgorithm-of-process includes (a) means for making more specific meaningof a verb from that of a verb whose meaning is more general.
 20. Theobject-oriented knowledge base system implemented in a digital computerof claim 16 wherein further (a) means for fusing propositions, whichprevents a combinatorial explosion, is used during carrying out saidmeans for carrying out a inference.
 21. The object-oriented knowledgebase system implemented in a digital computer of claim 16 wherein (a)means for storing data that strictly define objects, is used as saidmeans for storing data that define objects.
 22. The object-orientedknowledge base system implemented in a digital computer of claim 16wherein (a) means for storing data of ideal thesaurus in a formal way isused as said means for storing data of ideal thesaurus.
 23. Theobject-oriented knowledge base system implemented in a digital computerof claim 16 wherein (a) means for storing data of classification tablein a formal way is used as said means for storing data of classificationtable.
 24. The object-oriented knowledge base system implemented in adigital computer of claim 16 wherein (a) means for storing data used asrules in a formal way is used as said means for storing data used asrules
 25. The object-oriented knowledge base system implemented in adigital computer of claim 16 wherein (a) means for storing data aboutinstances of solving problems is used as said means for storing dataused as rules
 26. The object-oriented knowledge base system implementedin a digital computer of claim 16 wherein (a) means for describing afunction used as a rule is used as said means for storing data used asrules
 27. The object-oriented knowledge base system implemented in adigital computer of claim 26 wherein as the name of the function ofclaim 26, a name selected from the group consisting of (a) the name of asubroutine, (b) the name of a computer program, and, (c) the name of anunit operation described in a manual is used.
 28. The object-orientedknowledge base system implemented in a digital computer of claim 16wherein the body of information of said object-oriented knowledge basefurther includes (a) data comprising (a-1) a plurality of keys describedusing means for storing data providing the ability of association. 29.The object-oriented knowledge base system implemented in a digitalcomputer of claim 28 wherein (a) means for storing data providing theability of association in a strict way is used as said means for storingdata providing the ability of association.
 30. The object-orientedknowledge base system implemented in a digital computer of claim 16, insaid object-oriented knowledge base system comprising: (a) theobject-oriented knowledge whose body of information includes dataselected from the group consisting of (a-1) the ideal thesaurus in whichhierarchy of nouns is stored, comprising (a-1-1) the plurality of keysdescribed using means for storing data of ideal thesaurus, in which anoun with broader meaning is described as a broader descriptor, and anoun with narrower meaning is described as a narrower descriptor, (a-2)the ideal classification table in which hierarchy of verbs is recorded,comprising (a-2-1) the plurality of keys described using means forstoring data of classification table, in which a verb with broadermeaning is described as a higher class name-of-classification-item and averb with narrower meaning is described as a lower classname-of-classification-item, (a-3) data comprising (a-3-1) the pluralityof keys described using the means for storing data providing the abilityof association, and, (a-4) the rules described by using keys selectedfrom the group consisting of (a-4-1) the plurality of keys describedusing means for storing data used as rules, wherein the (a-4-1-1) meansfor describing sentences according to a simple English grammar. is used,and, (a-4-2) the plurality of keys described using the means for storingdata about instances of solving problems, (b) the object-orientedknowledge management system whose body of information includes dataselected from the group consisting of (b-1) the ideal dictionary whereinlexical definition of verbs and nouns are given by using keys selectedfrom the group consisting of (b-1-1) the plurality of keys describedusing means for storing the list of lexical meanings of a natural word,(b-1-2) the plurality of keys described using means for storing datathat define objects, for giving the lexical definition of an ideal noun,(b-1-3) the plurality of keys described using means for implementationof algorithms-of-processes, for giving the lexical definition of anideal verb, and, (b-1-4) the plurality of keys described using means fordescribing the function of a verb is used, for giving the lexicaldefinition of an ideal verb, (b-2) the means for giving definition ofbroader descriptor and narrower descriptor, coupled to saidobject-oriented knowledge base, for constructing the hierarchy of nounsin said ideal thesaurus wherein as the basis on which said definition ofbroader descriptor and narrower descriptor is given, (b-2-1) theplurality of keys described using means for storing data that defineobjects, are used, and, (b-3) the means for giving definition of higherclass algorithm-of-process and lower class algorithm-of-process, coupledto said object-oriented knowledge base, for constructing the hierarchyof verbs in said ideal classification table wherein as the basis onwhich said definition of higher class algorithm-of-process and lowerclass algorithm-of-process is given, (b-3-1) the plurality of keysdescribed using means for implementation of algorithms -of-processes,are used, and, (c) the means for carrying out a inference, coupled tosaid object-oriented knowledge base wherein as the style of thereasoning, (c-1) an opportunistic reasoning is adopted, whereinreasoning selected from the group consisting of (c-1-1) forwardreasoning and (c-1-2) backward reasoning is carried out in each step ofsaid opportunistic reasoning and, the aim of each step of saidopportunistic reasoning is to try to logically prove (c-1-3) ahypothetical proposition that is the target of the present step ofopportunistic reasoning, said hypothetical proposition which is thetarget of the present step of opportunistic reasoning having apresupposition and a consequence, and, (c-2) each step of saidopportunistic reasoning comprises set of means selected from the groupconsisting of, (c-2-1) means for getting rules-for-reasoning, (c-2-1-0)said rules-for-reasoning equals to (c-2-1-0-1) hypothetical propositionsto be used in forward reasoning in the case of forward reasoning, saidhypothetical propositions to be used in forward reasoning having apresupposition and a consequence, and equal to (c-2-1-0-2) hypotheticalpropositions to be used in backward reasoning in the case of backwardreasoning, said hypothetical propositions to be used in backwardreasoning having a presupposition and a consequence wherein algorithmsselected from the group consisting of, (c-2-1-1) means for gettingdescriptors that are used to make a query to get the candidates of therules-for-reasoning comprising (c-2-1-1-1) means for making a list ofdescriptors ranked in order of hit frequency wherein (c-2-1-1-1-1) thedescriptors in said list are ones associated with (c-2-1-1-1-1-1)anatural word which characterizes the presupposition of said hypotheticalproposition that is the target of the present step of opportunisticreasoning in the case of forward reasoning, and (c-2-1-1-1-1-2)a naturalword which characterizes the consequence of said hypotheticalproposition that is the target of the present step of opportunisticreasoning in the case of backward reasoning, and, (c-2-1-1-1-2) as thebasis from which the descriptors which are to be used to make(c-2-1-1-1-2-1) a query to get the candidates of therules-for-reasoning, are fetched, the data selected from the groupconsisting of (c-2-1-1-1-2-2) the plurality of keys described usingmeans for storing data providing the ability of association,(c-2-1-1-1-2-3) the plurality of keys described using means for storingthe list of lexical meanings of a natural word, and, (c-2-1-1-1-2-4)other plurality of keys in said object-oriented knowledge base, areused, (c-2-1-2) means for getting the names-of-classification-items thatare used to make said query to get the candidates of therules-for-reasoning comprising (c-2-1-2-1) means for making a list ofnames-of-classification-items ranked in order of hit frequency wherein(c-2-1-2-1-1) the names-of-classification-items in said list are onesassociated with (c-2-1-2-1-1-1)a natural word which characterizes thepresupposition of said hypothetical proposition that is the target ofthe present step of opportunistic reasoning in the case of forwardreasoning, and, (c-2-1-2-1-1-2) a natural word that characterizes theconsequence of said hypothetical proposition which is the target of thepresent step of reasoning in the case of backward reasoning, and,(c-2-1-2-1-2) as the basis from which the names-of-classification-itemsthat are to be used to make (c-2-1-2-1-2-1) said query to get thecandidates of the rules-for-reasoning, are fetched, the data selectedfrom the group consisting of (c-2-1-2-1-2-2) the plurality of keysdescribed using means for storing data providing the ability ofassociation, (c-2-1-2-1-2-3) the plurality of keys described using meansfor storing the list of lexical meanings of a natural word, and,(c-2-1-2-1-2-4) other plurality of keys in said object-orientedknowledge base, are used, (c-2-1-3) a retrieval of the candidates of therules-for-reasoning using said query selected from the group consistingof the query of (c-2-1-1) and the query of (c-2-1-2) wherein saidcandidates of the rules-for-reasoning is a hypothetical proposition,whose presupposition is hit upon during a Boolean search using thedescriptors of (c-2-1-1) and the names-of-classification-items of(c-2-1-2) in the in the case of forward reasoning and whose consequenceis hit upon during a Boolean search using the descriptors of (c-2-1-1)and the names-of-classification-items of (c-2-1-2) in the case ofbackward reasoning, and said candidates of the rules-for-reasoning areretrieved from the basis comprising the data selected from the groupconsisting of (c-2-1-3-1) the plurality of keys described using meansfor storing data used as rules, (c-2-1-3-2) the plurality of keysdescribed using the means for storing data about instances of solvingproblems, and, (c-2-1-3-3) the plurality of keys described using meansfor describing a function used as a rule, are used, (c-2-1-4) means forpicking up only the rules-for-reasoning from the candidates of therules-for-reasoning got in the retrieval of (c-2-1-3) wherein out of thecandidates of the rules-for-reasoning, only (c-2-1-4-1) the rules whosepresupposition is derived from the presupposition of the hypotheticalproposition that is the target of the present step of opportunisticreasoning, by using (c-2-1-4-1-1) means for carrying out sentence basedobject-oriented hypothetical syllogism are picked out as therules-for-reasoning, in the case of forward reasoning, and, only(c-2-1-4-2) the rules from whose consequence, the consequence of thehypothetical proposition that is the target of the present step ofopportunistic reasoning is derived by using the (c-2-1-4-2-1) means forcarrying out sentence based object-oriented hypothetical syllogism, arepicked out as the rules-for-reasoning, in the case of backwardreasoning, and, said means for picking up only the rules-for-reasoningfrom the candidates of the rules-for-reasoning got in the retrieval of(c-2-1-3) is defined by using the (c-2-1-4-3) means for carrying outsentence based object-oriented hypothetical syllogism the procedure ofwhich is defined on the basis of the (c-2-1-4-3-1) means for carryingout sentence based object-oriented categorical syllogism wherein as thebasis on which the sentence based object-oriented categorical syllogismis carried out, (c-2-1-4-3-1-1) said ideal thesaurus, (c-2-1-4-3-1-2)said ideal classification table, and, (c-2-1-4-3-1-3) said hypotheticalproposition which is the target of the present step of opportunisticreasoning, are used, and, (c-2-1-5) means for retrieving directly therules-for-reasoning wherein as the basis from which therules-for-reasoning are retrieved, data selected from the groupconsisting of (c-2-1-5-1) the plurality of keys described using meansfor storing data used as rules, (c-2-1-5-2) the plurality of keysdescribed using the means for storing data about instances of solvingproblems, and, (c-2-1-5-3) the plurality of keys described using meansfor describing a function used as a rule, are used, to retrieverules-for-reasoning, whose presupposition is derived by using the(c-2-1-5-4) means for carrying out sentence based object-orientedhypothetical syllogism from the presupposition of the (c-2-1-5-5)hypothetical proposition which is the target of the present step ofopportunistic reasoning, in the case of forward reasoning, and, toretrieve rules-for-reasoning, from whose consequence, the consequence ofthe (c-2-1-5-6) hypothetical proposition which is the target of thepresent step of opportunistic reasoning is derived by using the(c-2-1-5-7) means for carrying out sentence based object-orientedhypothetical syllogism, in the case of backward reasoning wherein theprocedure for means for carrying out sentence based object-orientedhypothetical syllogism is defined on the basis of (c-2-1-5-8) means forcarrying out sentence based object-oriented categorical syllogismwherein as the basis on which the sentence based object-orientedcategorical syllogism is carried out, (c-2-1-5-8-1) said idealthesaurus, (c-2-1-5-8-2) said ideal classification table, and,(c-2-1-5-8-3) said hypothetical proposition which is the target of thepresent step of opportunistic reasoning, are used, (c-2-2) means foravoiding combinatorial explosion when too many number of saidrules-for-reasoning are retrieved wherein means selected from the groupconsisting of, (c-2-2-1) means for narrowing down the targetdescriptors, (c-2-2-2) means for narrowing down the target names-of-classification-items and (c-2-2-3) means for fusing propositions, areused, and then, the rules-for-reasoning are retrieved again, (c-2-3)means for making more exhaustive retrieval when too less number of saidrules-for-reasoning are retrieved wherein means selected from the groupconsisting of, (c-2-3-1) means for broadening out the target descriptorsand (c-2-3-2) means for broadening out the targetnames-of-classification-items, are used, and then, therules-for-reasoning are retrieved again, and, (c-2-4) means fordetermining the hypothetical propositions that are to be used as thetarget of the next step of opportunistic reasoning, as the basis fromwhich said hypothetical propositions which are to be used as the targetof the next step of opportunistic reasoning are retrieved, data selectedfrom the group consisting of (c-2-4-1) said rules-for-reasoning, and,(c-2-4-2) said hypothetical proposition which is the target of thepresent step of opportunistic reasoning, are used, whereby, saidobject-oriented knowledge base system processes not only rules andquestions written as mathematically well defined equations but alsorules and questions written as English sentences written in sentencepattern of one of five basic sentence patterns of English grammar, andgives answers not only written as mathematically well defined equationsbut also written as English sentences written in sentence pattern of oneof five basic sentence patterns of English grammar.
 31. A method ofconstructing an object-oriented knowledge base system comprising (a)providing an object-oriented knowledge base whose body of information isgiven by using the method selected from the group consisting of (a-1)giving an ideal thesaurus in which hierarchy of nouns is stored, byusing the method comprising (a-1-1) describing a plurality of keys usingmeans for storing data of ideal thesaurus, in which a noun with broadermeaning is described as a broader descriptor, and a noun with narrowermeaning is described as a narrower descriptor, (a-2) giving an idealclassification table in which hierarchy of verbs is recorded, by usingthe method comprising (a-2-1) describing a plurality of keys using meansfor storing data of classification table, in which a verb with broadermeaning is described as a higher class name-of-classification-item and averb with narrower meaning is described as a lower classname-of-classification-item, and, (a-3) giving rules by (a-3-1)describing a plurality of keys using means for storing data used asrules, (b) providing an object-oriented knowledge base management systemwhose body of information is given by using the method selected from thegroup consisting of (b-1) giving an ideal dictionary wherein as themethod to give lexical definition of verbs and nouns the method selectedfrom the group consisting of (b-1-1) describing a plurality of keysusing means for storing data that define objects, for giving the lexicaldefinition of an ideal noun, (b-1-2) describing a plurality of keysusing means for implementation of algorithms-of-processes, for givingthe lexical definition of an ideal verb, and, (b-1-3) describing aplurality of keys using means for describing the function of a verb, forgiving the lexical definition of an ideal verb, are used, (b-2) usingmeans for giving definition of broader descriptor and narrowerdescriptor for constructing the hierarchy of nouns in said idealthesaurus, by, (b-2-1) using the plurality of keys described using meansfor storing data that define objects, as the basis on which saiddefinition of broader descriptor and narrower descriptor is given, (b-3)using means for giving definition of higher class algorithm-of-processand lower class algorithm-of-process, for constructing the hierarchy ofverbs in said ideal classification table, by, (b-3-1) using theplurality of keys described using means for implementation ofalgorithms-of-processes, as the basis on which said definition of higherclass algorithm-of-process and lower class algorithm-of-process isgiven, and, (c) using a means for carrying out a inference, by (c-1)using means for carrying out sentence based object-oriented categoricalsyllogism wherein (c-1-1) using said ideal thesaurus, and (c-1-2) usingsaid ideal classification table, as the basis on which the sentencebased object-oriented categorical syllogism is carried out, whereby,said object-oriented knowledge base system processes not only rules andquestions written as mathematically well defined equations but alsorules and questions written as English sentences written in sentencepattern of one of five basic sentence patterns of English grammar. 32.The method of constructing an object-oriented knowledge base system ofclaim 31 wherein giving said ideal dictionary further using a method of(a) describing a plurality of keys using means for storing the list oflexical meanings of a natural word.
 33. The method of constructing anobject-oriented knowledge base system of claim 31 wherein said keysdescribed using means for storing data used as rules comprises aplurality of keys described according to (a) means for describingsentences according to a simple English grammar.
 34. The method ofconstructing an object-oriented knowledge base system of claim 31wherein using means for giving definition of higher classalgorithm-of-process and lower class algorithm-of-process by (a) usingmeans for making more specific meaning of a verb from that of a verbwhose meaning is more general.
 35. The method of constructing anobject-oriented knowledge base system of claim 31 wherein (a) usingmeans for fusing propositions, which prevents a combinatorial explosion,when carrying out a inference.
 36. The method of constructing anobject:oriented knowledge base system of claim 31 wherein (a) means forstoring data that strictly define objects, is used as said means forstoring data that define objects.
 37. The method of constructing anobject-oriented knowledge base system of claim 31 wherein (a) means forstoring data of ideal thesaurus in a formal way is used as said meansfor storing data of ideal thesaurus.
 38. The method of constructing anobject-oriented knowledge base system of claim 31 wherein (a) means forstoring data of ideal classification table in a formal way is used assaid means for storing data of classification table.
 39. The method ofconstructing an object-oriented knowledge base system of claim 31wherein (a) means for storing data used as rules in a formal way is usedas said means for storing data used as rules.
 40. The method ofconstructing an object-oriented knowledge base system of claim 31wherein (a) means for storing data about instances of solving problems,is used as said means for storing data used as rules.
 41. The method ofconstructing an object-oriented knowledge base system of claim 31wherein (a) means for describing a function used as a rule is used assaid means for storing data used as rules.
 42. The method ofconstructing an object-oriented knowledge base system of claim 41wherein as the name of the function of claim 41, a name selected fromthe group consisting of (a) the name of a subroutine, (b) the name of acomputer program, and, (c) the name of an unit operation described in amanual is used.
 43. The method of constructing an object-orientedknowledge base system of claim 31 wherein the body of information ofsaid object-oriented knowledge base further includes (a) data comprising(a-1) a plurality of keys described using means for storing dataproviding the ability of association.
 44. The method of constructing anobject-oriented knowledge base system of claim 43 wherein (a) means forstoring data providing the ability of association in a strict way isused as said means for storing data providing the ability ofassociation.
 45. A method of constructing an object-oriented knowledgebase system of claim 31, comprising (a) providing the object-orientedknowledge base wherein the body of information is given by using themethod selected from the group consisting of (a-1) giving the idealthesaurus in which hierarchy of nouns is stored, by using the methodcomprising (a-1-1) describing the plurality of keys using means forstoring data of ideal thesaurus, in which a noun with broader meaning isdescribed as a broader descriptor, and a noun with narrower meaning isdescribed as a narrower descriptor, (a-2) giving the idealclassification table in which hierarchy of verbs is recorded, by usingthe method comprising (a-2-1) describing the plurality of keys usingmeans for storing data of classification table, in which a verb withbroader meaning is described as a higher classname-of-classification-item and a verb with narrower meaning isdescribed as a lower class name-of-classification-item, (a-3) giving thedata by using the method comprising (a-3-1) describing the plurality ofkeys using the means for storing data providing the ability ofassociation, and, (a-4) giving the rules by using the method selectedfrom the group consisting of (a-4-1) describing the plurality of keysusing means for storing data used as rules wherein (a-4-1-1) using themeans for describing sentences according to a simple English grammar. asthe way to describe a sentence in the rule, and, (a-4-2) describing theplurality of keys described using the means for storing data aboutinstances of solving problems, (b) providing the object-orientedknowledge base wherein the body of information is given by using themethod selected from the group consisting of (b-1) giving the idealdictionary wherein as the method to give lexical definition of verbs andnouns the method selected from the group consisting of (b-1-1)describing the plurality of keys using means for storing the list oflexical meanings of a natural word, (b-1-2) describing the plurality ofkeys using means for storing data that define objects, for giving thelexical definition of an ideal noun, (b-1-3) describing the plurality ofkeys using means for implementation of algorithms-of-processes, forgiving the lexical definition of an ideal verb, and, (b-1-4) describingthe plurality of keys using means for describing the function of a verbis used, for giving the lexical definition of an ideal verb, are used,(b-2) using means for giving definition of broader descriptor andnarrower descriptor for constructing the hierarchy of nouns in saidideal thesaurus, by (b-2-1) using the plurality of keys described usingmeans for storing data that define objects, as the basis on which saiddefinition of broader descriptor and narrower descriptor is given, and,(b-3) using means for giving definition of higher classalgorithm-of-process and lower class algorithm-of-process, coupled tosaid object-oriented knowledge base, for constructing the hierarchy ofverbs in said ideal classification table, by, (b-3-1) using theplurality of keys described using means for implementation ofalgorithms-of-processes, as the basis on which said definition of higherclass algorithm-of-process and lower class algorithm-of-process isgiven, and, (c) using the means for carrying out a inference that iscoupled to said object-oriented knowledge base wherein (c-1) using anopportunistic reasoning as the style of reasoning wherein carrying out areasoning selected from the group consisting of (c-1-1) forwardreasoning and (c-1-2) backward reasoning in each step of saidopportunistic reasoning and, as the aim of each step of saidopportunistic reasoning, (c-1-3) trying to logically prove ahypothetical proposition which is the target of the present step ofopportunistic reasoning, said hypothetical proposition which is thetarget of the present step of opportunistic reasoning having apresupposition and a consequence, and, (c-2) carrying out each step ofsaid opportunistic reasoning comprises set of methods selected from thegroup consisting of, (c-2-1) using means for gettingrules-for-reasoning, providing said rules-for reasoning by (c-2-1-1)providing hypothetical propositions to be used in forward reasoning inthe case of forward reasoning, said hypothetical propositions to be usedin forward reasoning having a presupposition and a consequence, and by(c-2-1-2) providing hypothetical propositions to be used in backwardreasoning in the case of backward reasoning, said hypotheticalpropositions to be used in backward reasoning having a presuppositionand a consequence wherein methods selected from the group consisting of,(c-2-1-1) using means for getting descriptors that are used to make aquery to get the candidates of the rules-for-reasoning wherein(c-2-1-1-1) using means for making a list of descriptors ranked in orderof hit frequency wherein (c-2-1-1-1-1) judging a descriptor to be thedescriptors in said list if it is associated with a natural word whichcharacterizes the presupposition of said hypothetical proposition thatis the target of the present step of opportunistic reasoning in the caseof forward reasoning, and a natural word which characterizes theconsequence of said hypothetical proposition that is the target of thepresent step of opportunistic reasoning in the case of backwardreasoning, and, (c-2-1-1-1-2) as the way of using basis from which thedescriptors that are to be used for (c-2-1-1-1-2-1) making a query toget the candidates of the rules-for-reasoning, are fetched, methodsselected from the group consisting of (c-2-1-1-1-2-2) using theplurality of keys described using the means for storing data providingthe ability of association, (c-2-1-1-1-2-3) using the plurality of keysdescribed using the means for storing the list of lexical meanings of anatural word, and, (c-2-1-1-1-2-4) using other plurality of keys in saidobject-oriented knowledge base, are used, (c-2-1-2) using means forgetting the names-of-classification-items that are used to make saidquery to get the candidates of the rules-for-reasoning wherein(c-2-1-2-1) using means for making a list ofnames-of-classification-items ranked in order of hit frequency wherein(c-2-1-2-1-1) judging a names-of-classification-item to be thenames-of-classification-item in said list if it is associated with anatural word which characterizes the presupposition of said hypotheticalproposition that is the target of the present step of opportunisticreasoning in the case of forward reasoning, and a natural word thatcharacterizes the consequence of said hypothetical proposition which isthe target of the present step of reasoning in the case of backwardreasoning, and (c-2-1-2-1-2) as the way of using basis from which thenames-of-classification-items that are to be used for (c-2-1-2-1-2-1)making said query to get the candidate of the rules-for-reasoning, arefetched, methods selected from the group consisting of (c-2-1-2-1-2-2)using the plurality of keys described using the means for storing dataproviding the ability of association, (c-2-1-2-1-2-3) using theplurality of keys described using the means for storing the list oflexical meanings of a natural word, and, (c-2-1-2-1-2-4) using otherplurality of keys in said object-oriented knowledge base, are used,(c-2-1-3) making a retrieval of the candidates ofthe-rules-for-reasoning using said query selected from the groupconsisting of the query of (c-2-1-1) and the query of (c-2-1-2) whereinjudging a hypothetical proposition to be said candidates of therules-for-reasoning if whose presupposition is hit upon during a Booleansearch using the descriptors of (c-2-1-1) and thenames-of-classification-items of (c-2-1-2) in the in the case of forwardreasoning and whose consequence is hit upon during a Boolean searchusing the descriptors of (c-2-1-1) and the names-of-classification-itemsof (c-2-1-2) in the case of backward reasoning, and as the way of usingbasis from which said candidates of the rules-for-reasoning areretrieved, methods selected from the group consisting of (c-2-1-3-1)using the plurality of keys described using means for storing data usedas rules, (c-2-1-3-2) using the plurality of keys described using themeans for storing data about instances of solving problems, and,(c-2-1-3-3) using the plurality of keys described using means fordescribing the function of a verb, are used, (c-2-1-4) using means forpicking up only the rules-for-reasoning from the candidates of therules-for-reasoning got in the retrieval of (c-2-1-3) wherein out of thecandidates of the rules-for-reasoning, only (c-2-1-4-1) picking out therules whose presupposition is derived from the presupposition of thehypothetical proposition which is the target of the present step ofopportunistic reasoning, as the rules-for-reasoning, by (c-2-1-4-1-1)using the means for carrying out sentence based object-orientedhypothetical syllogism, in the case of forward reasoning, and, only(c-2-1-4-2) picking out the rules from whose consequence, theconsequence of the hypothetical proposition that is the target of thepresent step of opportunistic reasoning is derived as therules-for-reasoning by (c-2-1-4-2-1) using the means for carrying outsentence based object-oriented hypothetical syllogism, in the case ofbackward reasoning, and, defining said means for picking up only therules-for-reasoning from the candidates of the rules-for-reasoning gotin the retrieval of (c-2-1-3) is by (c-2-1-4-3) using the means forcarrying out sentence based object-oriented hypothetical syllogism, theprocedure of which is defined by (c-2-1-4-3-1) using the means forcarrying out sentence based object-oriented categorical syllogismwherein as the way of using basis on which the sentence basedobject-oriented categorical syllogism is carried out, methods selectedfrom the group consisting of (c-2-1-4-3-1-1) using said ideal thesaurus,(c-2-1-4-3-1-2) using said ideal classification table, and,(c-2-1-4-3-1-3) using said hypothetical proposition which is the targetof the present step of opportunistic reasoning, are used, and, (c-2-1-5)using means for retrieving directly the rules-for-reasoning wherein asthe way of using basis from which the rules-for-reasoning are retrieved,methods selected from the group consisting of (c-2-1-5-1) using theplurality of keys described using means for storing data used as rules,(c-2-1-5-2) using the plurality of keys described using the means forstoring data about instances of solving problems, and, (c-2-1-5-3) usingthe plurality of keys described using means for describing the functionof a verb, are used, to retrieve rules-for-reasoning, whosepresupposition is derived by (c-2-1-5-4) using the means for carryingout sentence based object-oriented hypothetical syllogism from thepresupposition of the hypothetical proposition which is the target ofthe present step of opportunistic reasoning, in the case of forwardreasoning, and, to retrieve rules-for-reasoning from whose consequence,the consequence of the hypothetical proposition which is the target ofthe present step of opportunistic reasoning is derived by (c-2-1-5-5)using the means for carrying out sentence based object-orientedhypothetical syllogism, in the case of backward reasoning whereindefying the procedure for means for carrying out sentence basedobject-oriented hypothetical syllogism by (c-2-1-5-6) using means forcarrying out sentence based object-oriented categorical syllogismwherein as the way of using basis on which the sentence basedobject-oriented categorical syllogism is carried out, methods selectedfrom the group consisting of (c-2-1-5-6-1) using said ideal thesaurus,(c-2-1-5-6-2) using said ideal classification table, and, (c-2-1-5-6-3)using said hypothetical proposition which is the target of the presentstep of opportunistic reasoning, are used, (c-2-2) using means foravoiding combinatorial explosion when too many number of saidrules-for-reasoning are retrieved wherein methods selected from thegroup consisting of, (c-2-2-1) using means for narrowing down the targetdescriptors, (c-2-2-2) using means for narrowing down the targetnames-of-classification-items and (c-2-2-3) using means for fusingpropositions, are used, and then, retrieving the rules-for-reasoningagain, (c-2-3) using means for making more exhaustive retrieval when tooless number of said rules-for-reasoning are retrieved wherein methodsselected from the group consisting of, (c-2-3-1) using means forbroadening out the target descriptors and (c-2-3-2) using means forbroadening out the target names-of-classification-items, are used, andthen, retrieving the rules-for-reasoning again, and, (c-2-4) using meansfor determining the hypothetical propositions that are to be used as thetarget of the next step of opportunistic reasoning, as the way of usingbasis from which said hypothetical propositions which are to be used asthe target of the next step of opportunistic reasoning are retrieved,methods selected from the group consisting of (c-2-4-1) using saidrules-for-reasoning, and, (c-2-4-2) using said hypothetical propositionwhich is the target of the present step of opportunistic reasoning, areused, whereby, said object-oriented knowledge base system processes notonly rules and questions written as mathematically well definedequations but also rules and questions written as English sentenceswritten in sentence pattern of one of five basic sentence patterns ofEnglish grammar, and gives answers not only written as mathematicallywell defined equations but also written as English sentences written insentence pattern of one of five basic sentence patterns of Englishgrammar.
 46. An object-oriented knowledge base system in the inferencemechanism of which means for carrying out sentence based object-orientedcategorical syllogism is used.
 47. Means for storing knowledge basesystem in which a set selected from the group consisting of theuniversal set and the subsets, of a plurality of data which constitutethe object-oriented knowledge base system of claim 46 is stored.